www.palgrave-journals.com/jors Forecasting and operational research: a review
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paper_title: Rule-based forecasting: Development and validation of an expert-systems approach to combining time-series extrapolations

paper_content:

This paper examines the feasibility of rule -based forecasting, a procedure that applies forecasting expertise and domain knowledge to produce forecasts according to features of the data. We developed a rule base to make annual extrapolation forecasts for economic and demographic time series. The development of the rule base drew upon protocol analyses of five experts on forecasting methods. This rule base, consisting of 99 rules, combined forecasts from four extrapolation methods (the random walk, regression, Brown's linear exponential smoothing, and Holt's exponential smoothing) according to rules using 18 features of time series. For one-year ahead ex ante forecasts of 90 annual series, the median absolute percentage error (MdAPE) for rule- based forecasting was 13% less than that from equally-weighted combined forecasts. For six-year ahead ex ante forecasts, rule-based forecasting had a MdAPE that was 42% less. The improvement in accuracy of the rule - based forecasts over equally-weighted combined forecasts was statistically significant. Rule-based forecasting was more accurate than equal-weights combining in situations involving significant trends, low uncertainty, stability, and good domain expertise.
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paper_title: Quantitative Forecasting — The State of the Art: Extrapolative Models

paper_content:

This paper sponsored by forecasting Study Group of the O.R. Society surveys the alternative extrapolative forecasting models regularly used and examines the various problems that arise. Extrapolative models are particularly appropriate when used for inventory control and materials purchase, and these two situations are considered. The paper concludes with an analysis of the evidence on the forecasting accuracy of extrapolative models.
---
paper_title: A unified view of statistical forecasting procedures

paper_content:

A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods, such as the exponentially weighted moving average, to more complex procedures such as Box–Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper sets out to show the relationship between these various procedures by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components. The framework is then extended to cover models with explanatory variables. From the technical point of view the Kalman filter plays an important role in allowing an integrated treatment of these topics.
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paper_title: Fuzzy versus statistical linear regression

paper_content:

Abstract Statistical linear regression and fuzzy linear regression have been developed from different perspectives, and thus there exist several conceptual and methodological differences between the two approaches. The characteristics of both methods, in terms of basic assumptions, parameter estimation, and application are described and contrasted. Their descriptive and predictive capabilities are also compared via a simulation experiment to identify the conditions under which one outperforms the other. It turns out that statistical linear regression is superior to fuzzy linear regression in terms of predictive capability, whereas their comparative descriptive performance depends on various factors associated with the data set (size, quality) and proper specificity of the model (aptness of the model, heteroscedasticity, autocorrelation, nonrandomness of error terms). Specifically, fuzzy linear regression performance becomes relatively better, vis-a-vis statistical linear regression, as the size of the data set diminishes and the aptness of the regression model deteriorates. Fuzzy linear regression may thus be used as a viable alternative to statistical linear regression in estimating regression parameters when the data set is insufficient to support statistical regression analysis and/or the aptness of the regression model is poor (e.g., due to vague relationship among variables and poor model specification).
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paper_title: Quantitative Forecasting—the State of the Art: Econometric Models

paper_content:

Econometric model building for forecasting has developed substantially over the past 15 years. Nevertheless, operational researchers have neglected these new developments. This paper defines nine stages the forecaster must go through when developing an econometric model. Each stage is discussed with reference to the most recent research. The complexity of econometric model building requires the forecaster to adopt a modelling strategy. Two have been proposed in the literature; one moves from a specific model to a more general version, the second goes from the general to the specific. Five recent O.R. studies using econometric methodology are then evaluated, highlighting the common areas of weakness. They are those areas that econometricians have paid most attention to in their recent research. The paper then surveys the effectiveness of econometric models compared to alternative approaches, concluding with some recommendations to the O.R. worker in forecasting. In particular, it recommends that more attempts are made to develop econometric models for O.R. applications.
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paper_title: Forecasting the Effects of Television Programming upon Electricity Loads

paper_content:

In Britain, the demand for electricity has been observed to rise sharply at the end of popular television programmes. This presents a serious forecasting problem for the Central Electricity Generating Board in the economic scheduling of standby reserve generation. This report provides some insights into the determinants of the problem and a multivariate forecasting aid through regression analyses on television audience measurements.
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paper_title: Forecasting Trends in Time Series

paper_content:

Most time series methods assume that any trend will continue unabated, regardless of the forecast lead time. But recent empirical findings suggest that forecast accuracy can be improved by either damping or ignoring altogether trends which have a low probability of persistence. This paper develops an exponential smoothing model designed to damp erratic trends. The model is tested using the sample of 1,001 time series first analyzed by Makridakis et al. Compared to smoothing models based on a linear trend, the model improves forecast accuracy, particularly at long leadtimes. The model also compares favorably to sophisticated time series models noted for good long-range performance, such as those of Lewandowski and Parzen.
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paper_title: The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion

paper_content:

Abstract In 1982, the International Institute of Forecasters set up the Journal of Forecasting , followed in 1985 by the International Journal of Forecasting. The primary aim of their foundation, laid out in the first issues, was to take a “multi-disciplinary perspective”; all types of forecasting methods were of interest. Of particular importance were “papers that compare[d] different approaches to actual forecasting situations”, the multiple hypotheses approach. This paper evaluates the success of the two journals in meeting their objectives and in setting the research agenda in forecasting. The approach taken is through citation analysis and the identification of influential forecasting articles using both citations and expert analysis. The two approaches identified the same themes as particularly important, with the econometric advances of Engle and Granger outstanding. A content analysis of the journals was also undertaken, showing that the comparative approach to establishing improved forecasting methods through examining multiple hypotheses has been successfully adopted and is unusual when compared to other journals. Few articles examined the conditions under which one approach outperformed its competitors. By examining the highly cited articles in the forecasting journals compared to other journals in the business, economics, and management area, I conclude that the forecasting journals have covered all areas of forecasting research; however, many influential articles are published across a wide range of other journals. There was little cross-fertilisation between journals. There remain, however, topics which have been widely neglected. In particular, organisational issues and the effects of forecast error, highlighted as important areas when the journals were founded, have been ignored. These two issues directly impact the gap between theoretical contributions and forecasting practice, a gap that remains unbridged. In short, the journals have made progress in meeting the objectives set by the founders, but there still remains much important research to be done.
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paper_title: Managerial Applications of Neural Networks: The Case of Bank Failure Predictions

paper_content:

This paper introduces a neural-net approach to perform discriminant analysis in business research. A neural net represents a nonlinear discriminant function as a pattern of connections between its processing units. Using bank default data, the neural-net approach is compared with linear classifier, logistic regression, kNN, and ID3. Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness. Limitations of using neural nets as a general modeling tool are also discussed.
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paper_title: Forecasting for Items with Intermittent Demand

paper_content:

In a service environment, a stockist usually has many slow moving items whose infrequency of demand can give rise to forecasting problems. Moreover, when a demand occurs, the request is sometimes for more than a single unit, which results in so-called lumpy demand. In this paper, the standard method for dealing with such intermittent demand is reassessed. Some general results are presented that enable variance estimates to be made, and these are particularly straightforward when the demand occasions can be represented as a Poisson process. Some experimental evidence is advanced to support this model in the specific situation under study. Since EWMA forecasts are central to many commercial systems, a simulation analysis was conducted to determine under what conditions intermittent demand requires its own model, rather than an unadjusted EWMA. Superior performance is demonstrated for items that have an average inter-order interval greater than 1.25 forecast review periods, and the magnitude of the improvement increases as the average interval lengthens.
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paper_title: Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times and Information

paper_content:

An important observation in supply chain management, known as the bullwhip effect, suggests that demand variability increases as one moves up a supply chain. In this paper we quantify this effect for simple, two-stage supply chains consisting of a single retailer and a single manufacturer. Our model includes two of the factors commonly assumed to cause the bullwhip effect: demand forecasting and order lead times. We extend these results to multiple-stage supply chains with and without centralized customer demand information and demonstrate that the bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information.
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paper_title: A unified view of statistical forecasting procedures

paper_content:

A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods, such as the exponentially weighted moving average, to more complex procedures such as Box–Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper sets out to show the relationship between these various procedures by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components. The framework is then extended to cover models with explanatory variables. From the technical point of view the Kalman filter plays an important role in allowing an integrated treatment of these topics.
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paper_title: Neural Networks: A New Tool for Predicting Thrift Failures*

paper_content:

A neural network model that processes input data consisting of financial ratios is developed to predict the financial health of thrift institutions. The network's ability to discriminate between healthy and failed institutions is compared to a traditional statistical model. The differences and similarities in the two modelling approaches are discussed. The neural network, which uses the same financial data, requires fewer assumptions, achieves a higher degree of prediction accuracy, and is more robust.
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paper_title: Quantitative Forecasting—the State of the Art: Econometric Models

paper_content:

Econometric model building for forecasting has developed substantially over the past 15 years. Nevertheless, operational researchers have neglected these new developments. This paper defines nine stages the forecaster must go through when developing an econometric model. Each stage is discussed with reference to the most recent research. The complexity of econometric model building requires the forecaster to adopt a modelling strategy. Two have been proposed in the literature; one moves from a specific model to a more general version, the second goes from the general to the specific. Five recent O.R. studies using econometric methodology are then evaluated, highlighting the common areas of weakness. They are those areas that econometricians have paid most attention to in their recent research. The paper then surveys the effectiveness of econometric models compared to alternative approaches, concluding with some recommendations to the O.R. worker in forecasting. In particular, it recommends that more attempts are made to develop econometric models for O.R. applications.
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paper_title: Bankruptcy prediction using neural networks

paper_content:

Abstract Prediction of firm bankruptcies have been extensively studied in accounting, as all stakeholders in a firm have a vested interest in monitoring its financial performance. This paper presents an exploratory study which compares the predictive capabilities for firm bankruptcy of neural networks and classical multivariate discriminant analysis. The predictive accuracy of the two techniques is presented within a comprehensive, statistically sound framework, indicating the value added to the forecasting problem by each technique. The study indicates that neural networks perform significantly better than discriminant analysis at predicting firm bankruptcies. Implications of our results for the accounting professional, neural networks researcher and decision support system builders are highlighted.
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paper_title: The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion

paper_content:

Abstract In 1982, the International Institute of Forecasters set up the Journal of Forecasting , followed in 1985 by the International Journal of Forecasting. The primary aim of their foundation, laid out in the first issues, was to take a “multi-disciplinary perspective”; all types of forecasting methods were of interest. Of particular importance were “papers that compare[d] different approaches to actual forecasting situations”, the multiple hypotheses approach. This paper evaluates the success of the two journals in meeting their objectives and in setting the research agenda in forecasting. The approach taken is through citation analysis and the identification of influential forecasting articles using both citations and expert analysis. The two approaches identified the same themes as particularly important, with the econometric advances of Engle and Granger outstanding. A content analysis of the journals was also undertaken, showing that the comparative approach to establishing improved forecasting methods through examining multiple hypotheses has been successfully adopted and is unusual when compared to other journals. Few articles examined the conditions under which one approach outperformed its competitors. By examining the highly cited articles in the forecasting journals compared to other journals in the business, economics, and management area, I conclude that the forecasting journals have covered all areas of forecasting research; however, many influential articles are published across a wide range of other journals. There was little cross-fertilisation between journals. There remain, however, topics which have been widely neglected. In particular, organisational issues and the effects of forecast error, highlighted as important areas when the journals were founded, have been ignored. These two issues directly impact the gap between theoretical contributions and forecasting practice, a gap that remains unbridged. In short, the journals have made progress in meeting the objectives set by the founders, but there still remains much important research to be done.
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paper_title: Forecasting Methods for Marketing: Review of Empirical Research

paper_content:

This paper reviews the empirical research on forecasting in marketing. In addition, it presents results from some small scale surveys. We offer a framework for discussing forecasts in the area of marketing, and then review the literature in light of that framework. Particular emphasis is given to a pragmatic interpretation of the literature and findings. Suggestions are made on what research is needed.
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paper_title: The Combination of Forecasts: a Bayesian Approach

paper_content:

This paper develops a general Bayesian approach to the problem of combining forecasts. This approach leads to the results of Bates and Granger in certain special cases and to a geometric averaging formula in other special cases.
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paper_title: Exponential smoothing: The state of the art--Part II

paper_content:

Abstract In Gardner [Gardner, E. S., Jr. (1985). Exponential smoothing: The state of the art. Journal of Forecasting 4, 1–28], I reviewed the research in exponential smoothing since the original work by Brown and Holt. This paper brings the state of the art up to date. The most important theoretical advance is the invention of a complete statistical rationale for exponential smoothing based on a new class of state-space models with a single source of error. The most important practical advance is the development of a robust method for smoothing damped multiplicative trends. We also have a new adaptive method for simple smoothing, the first such method to demonstrate credible improved forecast accuracy over fixed-parameter smoothing. Longstanding confusion in the literature about whether and how to renormalize seasonal indices in the Holt–Winters methods has finally been resolved. There has been significant work in forecasting for inventory control, including the development of new predictive distributions for total lead-time demand and several improved versions of Croston's method for forecasting intermittent time series. Regrettably, there has been little progress in the identification and selection of exponential smoothing methods. The research in this area is best described as inconclusive, and it is still difficult to beat the application of a damped trend to every time series.
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paper_title: Quantitative Forecasting — The State of the Art: Extrapolative Models

paper_content:

This paper sponsored by forecasting Study Group of the O.R. Society surveys the alternative extrapolative forecasting models regularly used and examines the various problems that arise. Extrapolative models are particularly appropriate when used for inventory control and materials purchase, and these two situations are considered. The paper concludes with an analysis of the evidence on the forecasting accuracy of extrapolative models.
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paper_title: Forecasting with Exponential Smoothing: The State Space Approach

paper_content:

I. Introduction: Basic concepts.- Getting started. II. Essentials: Linear innovations state space models.- Non-linear and heteroscedastic innovations state space models.- Estimation of innovations state space models.- Prediction distributions and intervals.- Selection of models. III. Further topics: Normalizing seasonal components.- Models with regressor variables.- Some properties of linear models.- Reduced forms and relationships with ARIMA models.- Linear innovations state space models with random seed states.- Conventional state space models.- Time series with multiple seasonal patterns.- Non-linear models for positive data.- Models for count data.- Vector exponential smoothing. IV. Applications: Inventory control application.- Conditional heteroscedasticity and finance applications.- Economic applications: the Beveridge-Nelson decomposition.
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paper_title: Exponential smoothing with a damped multiplicative trend

paper_content:

Multiplicative trend exponential smoothing has received very little attention in the literature. It involves modelling the local slope by smoothing successive ratios of the local level, and this leads to a forecast function that is the product of level and growth rate. By contrast, the popular Holt method uses an additive trend formulation. It has been argued that more real series have multiplicative trends than additive. However, even if this is true, it seems likely that the more conservative forecast function of the Holt method will be more robust when applied in an automated way to a large batch of series with different types of trend. In view of the improvements in accuracy seen in dampening the Holt method, in this paper we investigate a new damped multiplicative trend approach. An empirical study, using the monthly time series from the M3-Competition, gave encouraging results for the new approach at a range of forecast horizons, when compared to the established exponential smoothing methods.
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paper_title: A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods

paper_content:

We provide a new approach to automatic business forecasting based on an extended range of exponential smoothing methods. Each method in our taxonomy of exponential smoothing methods can be shown to be equivalent to the forecasts obtained from a state space model. This allows (1) the easy calculation of the likelihood, the AIC and other model selection criteria; (2) the computation of prediction intervals for each method; and (3) random simulation from the underlying state space model. We demonstrate the methods by applying them to the data from the M-competition and the M3-competition.
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paper_title: The accuracy of extrapolation (time series) methods: Results of a forecasting competition

paper_content:

ln the last few decades matiy methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.
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paper_title: A unified view of statistical forecasting procedures

paper_content:

A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods, such as the exponentially weighted moving average, to more complex procedures such as Box–Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper sets out to show the relationship between these various procedures by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components. The framework is then extended to cover models with explanatory variables. From the technical point of view the Kalman filter plays an important role in allowing an integrated treatment of these topics.
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paper_title: The accuracy of a procedural approach to specifying feedforward neural networks for forecasting

paper_content:

The comparative accuracy of feedforward neural networks (NN) when applied to time series forecasting problems remains uncertain. This is because most studies suffer from either of two defects--they choose the NN from a wide range of alternatives in order to present the forecast accuracy results in the best light or they do not compare the results with suitable benchmarks. In order to overcome both these objections, this paper proposes an objective procedure for specifying a feedforward NN model and evaluates its effectiveness by examining its forecasting performance compared with established benchmarks. After the selection of input nodes based on cross-validation, a three-stage procedure is proposed here which consists of sequentially selecting first the learning rate followed by the number of nodes and the initial weights. This paper shows that NNs only perform robustly if they are built by considering these three factors jointly. In an empirical demonstration of the strength of the approach, those NN models, built by considering all three factors, performed better than other competitive statistical methods when evaluated rigorously on a standard test data set.
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paper_title: Use of individual and group seasonal indices in subaggregate demand forecasting

paper_content:

An individual seasonal indices (ISI) method and two group seasonal indices (GSI) methods proposed in the literature are compared, based on two models. Rules have been established to choose between these methods and insights are gained on the conditions under which one method outperforms the others. Simulation findings confirm that using the rules improves forecasting accuracy against universal application of these methods.
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paper_title: Comparison of seasonal estimation methods in multi-item short-term forecasting

paper_content:

Abstract This paper addresses the issue of estimating seasonal indices for multi-item, short-term forecasting, based upon both individual time series estimates and groups of similar time series. This development of the joint use of individual and group seasonal estimation is extended in two directions. One class of methods is derived from the procedures developed for combining forecasts. The second employs the general class of Stein Rules to obtain shrinkage estimates of seasonal components. A comparative evaluation has been undertaken of several versions of these methods, based upon a sample of retail sales data. The results favour these newly developed methods and provide some interesting insights for practical implementation.
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paper_title: Forecasting Trends in Time Series

paper_content:

Most time series methods assume that any trend will continue unabated, regardless of the forecast lead time. But recent empirical findings suggest that forecast accuracy can be improved by either damping or ignoring altogether trends which have a low probability of persistence. This paper develops an exponential smoothing model designed to damp erratic trends. The model is tested using the sample of 1,001 time series first analyzed by Makridakis et al. Compared to smoothing models based on a linear trend, the model improves forecast accuracy, particularly at long leadtimes. The model also compares favorably to sophisticated time series models noted for good long-range performance, such as those of Lewandowski and Parzen.
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paper_title: Evaluation of Aggregate and Individual Forecast Method Selection Rules

paper_content:

A major use of univariate forecasting methods lies in production control where there is a large number of series to be forecast. The appropriate choice of forecasting method has the potential for major cost savings through improved accuracy. Where a new method is to be compared to one already established two distinct approaches to selecting between the two can be considered: aggregate selection, where a single method is applied to all the time series, versus individual selection, where the particular method appropriate for each series is identified and used to forecast future observations for that series. This paper evaluates these two selection rules using 263 data series from a single organization. The results show the potential of "individual selection" and also the difficulty of attaining it. For short lead times "aggregate selection" achieves similar accuracy. For longer leads it is outperformed by "individual selection" and also undermined by sampling variability. As a consequence, "aggregate selection" must be carried out across a wide cross-section of series and across time. When this is done the results of this case study show that "aggregate selection" is both simpler than and of comparable accuracy to "individual selection."
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paper_title: Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program

paper_content:

Abstract We examine the effect of damping X-12-ARIMA's estimated seasonal variation on the accuracy of its seasonal adjustments of time series. Two methods for damping seasonals are proposed. In a simulation experiment, we generated time series data for each of 90 distinct experimental conditions that, in aggregate, characterize the variety of monthly series in the M3-competition. X-12-ARIMA consistently overestimated the actual seasonal variation by an amount consistent with statistical theory. Damping seasonals reduced X-12-ARIMA's estimation error by as much as 79% and under no conditions was estimation error increased beyond a trivial amount. Improvement depended primarily on the degree to which random variation in a series dominated seasonal variation. When the multiplicative X-12-ARIMA model did not match the data-generating model, overestimation was less for trend series than for series with no trend; otherwise the presence of trend had no discernible effect. One of the proposed methods was somewhat more accurate and robust, but more complex, than the other. In an analysis of real data—the 1428 monthly series of the M3-competition-damping X-12-ARIMA seasonals prior to forecasting (1) reduced the average forecasting MAPE by 4.9–1.4% and (2) improved forecasting accuracy for 59–65% of the series, depending on the forecasting horizon. This research suggests that damping X-12-ARIMA seasonals leads to more accurate seasonal adjustments of time series, thus providing a more reliable basis for policy-making, forecasting, and the evaluation of forecasting methods by researchers.
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paper_title: 25 years of time series forecasting

paper_content:

Abstract We review the past 25 years of research into time series forecasting. In this silver jubilee issue, we naturally highlight results published in journals managed by the International Institute of Forecasters ( Journal of Forecasting 1982–1985 and International Journal of Forecasting 1985–2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.
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paper_title: Causal Forces: Structuring Knowledge for Time-series Extrapolation

paper_content:

This paper examines a strategy for structuring one type of domain knowledge for use in extrapolation. It does so by representing information about causality and using this domain knowledge to select and combine forecasts. We use five categories to express causal impacts upon trends: growth, decay, supporting, opposing, and regressing. An identification of causal forces aided in the determination of weights for combining extrapolation forecasts. These weights improved average ex ante forecast accuracy when tested on 104 annual economic and demographic time series. Gains in accuracy were greatest when (1) the causal forces were clearly specified and (2) stronger causal effects were expected, as in longer- range forecasts. One rule suggested by this analysis was: “Do not extrapolate trends if they are contrary to causal forces.” We tested this rule by comparing forecasts from a method that implicitly assumes supporting trends (Holt’s exponential smoothing) with forecasts from the random walk. Use of the rule improved accuracy for 20 series where the trends were contrary; the MdAPE (Median Absolute Percentage Error) was 18% less for the random walk on 20 one-year ahead forecasts and 40% less for 20 six-year-ahead forecasts. We then applied the rule to four other data sets. Here, the MdAPE for the random walk forecasts was 17% less than Holt’s error for 943 short-range forecasts and 43% less for 723 long-range forecasts. Our study suggests that the causal assumptions implicit in traditional extrapolation methods are inappropriate for many applications.
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paper_title: Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models

paper_content:

Abstract A class of nonlinear state-space models, characterized by a single source of randomness, is introduced. A special case, the model underpinning the multiplicative Holt-Winters method of forecasting, is identified. Maximum likelihood estimation based on exponential smoothing instead of a Kalman filter, and with the potential to be applied in contexts involving non-Gaussian disturbances, is considered. A method for computing prediction intervals is proposed and evaluated on both simulated and real data.
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paper_title: Experience with Forecasting Univariate Time Series and the Combination of Forecasts

paper_content:

A number of procedures for forecasting a time series from its own current and past values are surveyed. Forecasting performances of three methodsBox-Jenkins, Holt-Winters and stepwise autoregression-are compared over a large sample of economic time series. The possibility of combining individual forecasts in the production of an overall forecast is explored, and we present empirical results which indicate that such a procedure can frequently be profitable.
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paper_title: Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation

paper_content:

Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced in this paper. These are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances. For such processes, the recent past gives information about the one-period forecast variance. A regression model is then introduced with disturbances following an ARCH process. Maximum likelihood estimators are described and a simple scoring iteration formulated. Ordinary least squares maintains its optimality properties in this set-up, but maximum likelihood is more efficient. The relative efficiency is calculated and can be infinite. To test whether the disturbances follow an ARCH process, the Lagrange multiplier procedure is employed. The test is based simply on the autocorrelation of the squared OLS residuals. This model is used to estimate the means and variances of inflation in the U.K. The ARCH effect is found to be significant and the estimated variances increase substantially during the chaotic seventies.
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paper_title: Forecasting Economic Time Series

paper_content:

This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted.
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paper_title: Quantitative Forecasting—the State of the Art: Econometric Models

paper_content:

Econometric model building for forecasting has developed substantially over the past 15 years. Nevertheless, operational researchers have neglected these new developments. This paper defines nine stages the forecaster must go through when developing an econometric model. Each stage is discussed with reference to the most recent research. The complexity of econometric model building requires the forecaster to adopt a modelling strategy. Two have been proposed in the literature; one moves from a specific model to a more general version, the second goes from the general to the specific. Five recent O.R. studies using econometric methodology are then evaluated, highlighting the common areas of weakness. They are those areas that econometricians have paid most attention to in their recent research. The paper then surveys the effectiveness of econometric models compared to alternative approaches, concluding with some recommendations to the O.R. worker in forecasting. In particular, it recommends that more attempts are made to develop econometric models for O.R. applications.
---
paper_title: The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion

paper_content:

Abstract In 1982, the International Institute of Forecasters set up the Journal of Forecasting , followed in 1985 by the International Journal of Forecasting. The primary aim of their foundation, laid out in the first issues, was to take a “multi-disciplinary perspective”; all types of forecasting methods were of interest. Of particular importance were “papers that compare[d] different approaches to actual forecasting situations”, the multiple hypotheses approach. This paper evaluates the success of the two journals in meeting their objectives and in setting the research agenda in forecasting. The approach taken is through citation analysis and the identification of influential forecasting articles using both citations and expert analysis. The two approaches identified the same themes as particularly important, with the econometric advances of Engle and Granger outstanding. A content analysis of the journals was also undertaken, showing that the comparative approach to establishing improved forecasting methods through examining multiple hypotheses has been successfully adopted and is unusual when compared to other journals. Few articles examined the conditions under which one approach outperformed its competitors. By examining the highly cited articles in the forecasting journals compared to other journals in the business, economics, and management area, I conclude that the forecasting journals have covered all areas of forecasting research; however, many influential articles are published across a wide range of other journals. There was little cross-fertilisation between journals. There remain, however, topics which have been widely neglected. In particular, organisational issues and the effects of forecast error, highlighted as important areas when the journals were founded, have been ignored. These two issues directly impact the gap between theoretical contributions and forecasting practice, a gap that remains unbridged. In short, the journals have made progress in meeting the objectives set by the founders, but there still remains much important research to be done.
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paper_title: Model Uncertainty, Data Mining and Statistical Inference

paper_content:

This paper takes a broad, pragmatic view of statistical inference to include all aspects of model formulation. The estimation of model parameters traditionally assumes that a model has a prespecified known form and takes no account of possible uncertainty regarding the model structure. This implicitly assumes the existence of a 'true' model, which many would regard as a fiction. In practice model uncertainty is a fact of life and likely to be more serious than other sources of uncertainty which have received far more attention from statisticians. This is true whether the model is specified on subject-matter grounds or, as is increasingly the case, when a model is formulated, fitted and checked on the same data set in an iterative, interactive way. Modern computing power allows a large number of models to be considered and data-dependent specification searches have become the norm in many areas of statistics. The term data mining may be used in this context when the analyst goes to great lengths to obtain a good fit. This paper reviews the effects of model uncertainty, such as too narrow prediction intervals, and the non-trivial biases in parameter estimates which can follow data-based modelling. Ways of assessing and overcoming the effects of model uncertainty are discussed, including the use of simulation and resampling methods, a Bayesian model averaging approach and collecting additional data wherever possible. Perhaps the main aim of the paper is to ensure that statisticians are aware of the problems and start addressing the issues even if there is no simple, general theoretical fix.
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paper_title: Active learning with statistical models

paper_content:

For many types of machine learning algorithms, one can compute the statistically "optimal" way to select training data. In this paper, we review how optimal data selection techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are computationally expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate. Empirically, we observe that the optimality criterion sharply decreases the number of training examples the learner needs in order to achieve good performance.
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paper_title: Optimization-based feature selection with adaptive instance sampling

paper_content:

Preprocessing the data to filter out redundant and irrelevant features is one of the most important steps in the data mining process. Careful feature selection may improve both the computational time of inducing subsequent models and the quality of those models. Using fewer features often leads to simpler and easier to interpret models, and selecting important feature can lead to important insights into the application. The feature selection problem is inherently a combinatorial optimization problem. This paper builds on a metaheuristic called the nested partitions method that has been shown to be particularly effective for the feature selection problem. Specifically, we focus on the scalability of the method and show that its performance is vastly improved by incorporating random sampling of instances. Furthermore, we develop an adaptive variant of the algorithm that dynamically determines the required sample rate. The adaptive algorithm is shown to perform very well when applied to a set of standard test problems.
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paper_title: The Nature of Statistical Learning Theory

paper_content:

Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
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paper_title: SMOTE: Synthetic Minority Over-sampling Technique

paper_content:

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of"normal"examples with only a small percentage of"abnormal"or"interesting"examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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paper_title: Comparing support vector machines with gaussian kernels to radial basis function classi ers

paper_content:

The support vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights, and threshold that minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by X-means clustering, and the weights are computed using error backpropagation. We consider three machines, namely, a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the United States postal service database of handwritten digits, the SV machine achieves the highest recognition accuracy, followed by the hybrid system. The SV approach is thus not only theoretically well-founded but also superior in a practical application.
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paper_title: Improving Generalization with Active Learning

paper_content:

Active learning differs from “learning from examples” in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples alone, giving better generalization for a fixed number of training examples.In this article, we consider the problem of learning a binary concept in the absence of noise. We describe a formalism for active concept learning called selective sampling and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers “useful.” We test our implementation, called an SG-network, on three domains and observe significant improvement in generalization.
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paper_title: Data Mining: Statistics and More?

paper_content:

Abstract Data mining is a new discipline lying at the interface of statistics, database technology, pattern recognition, machine learning, and other areas. It is concerned with the secondary analysis of large databases in order to find previously unsuspected relationships which are of interest or value to the database owners. New problems arise, partly as a consequence of the sheer size of the data sets involved, and partly because of issues of pattern matching. However, since statistics provides the intellectual glue underlying the effort, it is important for statisticians to become involved. There are very real opportunities for statisticians to make significant contributions.
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paper_title: Linear programming approaches for multicategory support vector machines

paper_content:

Abstract We consider linear programming approaches for support vector machines (SVM). The linear programming problems are introduced as an approximation of the quadratic programming problems commonly used in SVM. When we consider the kernel based nonlinear discriminators, the approximation can be viewed as kernel principle component analysis which generates an important subspace from the feature space characterized the kernel function. We show that any data points nonlinearly, and implicitly, projected into the feature space by kernel functions can be approximately expressed as points lying a low dimensional Euclidean space explicitly, which enables us to develop linear programming formulations for nonlinear discriminators. We also introduce linear programming formulations for multicategory classification problems. We show that the same maximal margin principle exploited in SVM can be involved into the linear programming formulations. Moreover, considering the low dimensional feature subspace extraction, we can generate nonlinear multicategory discriminators by solving linear programming problems. Numerical experiments on real world datasets are presented. We show that the fairly low dimensional feature subspace can achieve a reasonable accuracy, and that the linear programming formulations calculate discriminators efficiently. We also discuss a sampling strategy which might be crucial for huge datasets.
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paper_title: Selectively Acquiring Customer Information: A New Data Acquisition Problem and an Active Learning-Based Solution

paper_content:

This paper presents a new information acquisition problem motivated by business applications where customer data has to be acquired with a specific modeling objective in mind. In the last two decades, there has been substantial work in two different fieldsoptimal experimental design and machine learningthat has addressed the issue of acquiring data in a selective manner with a specific objective in mind. We show that the problem presented here is different from the classic model-based data acquisition problems considered thus far in the literature in both fields. Building on work in optimal experimental design and in machine learning, we develop a new active learning technique for the information acquisition problem presented in this paper. We demonstrate that the proposed method performs well based on results from applying this method across 20 Web usage and machine learning data sets.
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paper_title: Data Mining: An Overview from a Database Perspective

paper_content:

Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many di erent elds have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classi cation of the available data mining techniques is provided and a comparative study of such techniques is presented. Index Terms | Data mining, knowledge discovery, association rules, classi cation, data clustering, pattern matching algorithms, data generalization and characterization, data cubes, multiple-dimensional databases. J. Han was supported in part by the research grant NSERC-A3723 from the Natural Sciences and Engineering Research Council of Canada, the research grant NCE:IRIS/Precarn-HMI5 from the Networks of Centres of Excellence of Canada, and research grants from MPR Teltech Ltd. and Hughes Research Laboratories.
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paper_title: Neural Networks in Business: Techniques and Applications for the Operations Researcher

paper_content:

Abstract This paper presents an overview of the different types of neural network models which are applicable when solving business problems. The history of neural networks in business is outlined, leading to a discussion of the current applications in business including data mining, as well as the current research directions. The role of neural networks as a modern operations research tool is discussed. Scope and purpose Neural networks are becoming increasingly popular in business. Many organisations are investing in neural network and data mining solutions to problems which have traditionally fallen under the responsibility of operations research. This article provides an overview for the operations research reader of the basic neural network techniques, as well as their historical and current use in business. The paper is intended as an introductory article for the remainder of this special issue on neural networks in business.
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paper_title: Linear and Nonlinear Separation of Patterns by Linear Programming

paper_content:

A pattern separation problem is basically a problem of obtaining a criterion for distinguishing between the elements of two disjoint sets of patterns. The patterns are usually represented by points in a Euclidean space. One way to achieve separation is to construct a plane or a nonlinear surface such that one set of patterns lies on one side of the plane or the surface, and the other set of patterns on the other side. Recently, it has been shown that linear and ellipsoidal separation may be achieved by nonlinear programming. In this work it is shown that both linear and nonlinear separation may be achieved by linear programming.
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paper_title: Optimal techniques for class-dependent attribute discretization

paper_content:

Preprocessing of raw data has been shown to improve performance of knowledge discovery processes. Discretization of quantitative attributes is a key component of preprocessing and has the potential to greatly impact the efficiency of the process and the quality of its outcomes. In attribute discretization, the value domain of an attribute is partitioned into a finite set of intervals so that the attribute can be described using a small number of discrete representations. Discretization therefore involves two decisions, on the number of intervals and the placement of interval boundaries. Previous approaches for quantitative attribute discretization have used heuristic algorithms to identify partitions of the attribute value domain. Therefore, these approaches cannot be guaranteed to provide the optimal solution for the given discretization criterion and number of intervals. In this paper, we use linear programming (LP) methods to formulate the attribute discretization problem. The LP formulation allows the discretization criterion and the number of intervals to be integral considerations of the problem. We conduct experiments and identify optimal solutions for various discretization criteria and numbers of intervals.
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paper_title: Random Forests

paper_content:

Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
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paper_title: Bagging Predictors

paper_content:

Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.
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paper_title: Neural Networks for the MS/OR Analyst: An Application Bibliography

paper_content:

From a management scientist's perspective, the neural networks offer three opportunities: statistical methods, optimization methods, and a problem domain in which to apply OR algorithms. This annotated bibliography of application literature should offer a nontechnical fast track to someone getting started in this exciting area. The research published thus far on the applications of neural networks for statistical and optimization problems shows promise for the interface of neural networks and management science.
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paper_title: Using simulated annealing to optimize the feature selection problem in marketing applications

paper_content:

The feature selection (also, specification) problem is concerned with finding the most influential subset of predictors in predictive modeling from a much larger set of potential predictors that can contain hundreds of predictors. The problem belongs to the realm of combinatorial optimization where the objective is to find the subset of variables that optimize the value of some goodness of fit function. Due to the dimensionality of the problem, the feature selection problem belongs to the group of NP-hard problems. Most of the available predictors are noisy or redundant and add very little, if any, to the prediction power of the model. Using all the predictors in the model often results in strong over-fitting and very poor predictions. Constructing a prediction model by checking out all possible subsets is impractical due to computational volume. Looking on the contribution of each predictor separately is not accurate because it ignores the inter-correlations between predictors. As a result, no analytic solution is available for the feature selection problem, requiring that one resorts to heuristics. In this paper we employ the simulated annealing (SA) approach, which is one of the leading stochastic search methods, for specifying a large-scale linear regression model. The SA results are compared to the results of the more common stepwise regression (SWR) approach for model specification. The models are applied on realistic data sets in database marketing. We also use simulated data sets to investigate what data characteristics make the SWR approach equivalent to the supposedly more superior SA approach.
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paper_title: A training algorithm for optimal margin classifiers

paper_content:

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.
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paper_title: Cost-sensitive learning and decision making revisited

paper_content:

In many real-life decision making situations the default assumption of equal misclassification costs underlying pattern recognition techniques is most likely violated. Then, cost-sensitive learning and decision making bring help for making cost-benefit-wise optimal decisions. This paper brings an up-to-date overview of several methods that aim to make a broad variety of error-based learners cost-sensitive. More specifically, we revisit direct minimum expected cost classification, MetaCost, over- and undersampling, and cost-sensitive boosting.
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paper_title: A misclassification cost‐minimizing evolutionary–neural classification approach

paper_content:

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)-based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real-world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back-propagation artificial neural network, integrated cost preference-based linear mathematical programming-based minimize squared deviations, linear integrated cost preference-based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost-minimizing approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.
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paper_title: Statistical modeling: the two cultures

paper_content:

There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated bya given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical communityhas been committed to the almost exclusive use of data models. This commit- ment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current prob- lems. Algorithmic modeling, both in theoryand practice, has developed rapidlyin fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move awayfrom exclusive dependence on data models and adopt a more diverse set of tools.
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paper_title: Robust Classification for Imprecise Environments

paper_content:

In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.
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paper_title: The impact of preprocessing on data mining: an evaluation of classifier sensitivity in direct marketing

paper_content:

Abstract Corporate data mining faces the challenge of systematic knowledge discovery in large data streams to support managerial decision making. While research in operations research, direct marketing and machine learning focuses on the analysis and design of data mining algorithms, the interaction of data mining with the preceding phase of data preprocessing has not been investigated in detail. This paper investigates the influence of different preprocessing techniques of attribute scaling, sampling, coding of categorical as well as coding of continuous attributes on the classifier performance of decision trees, neural networks and support vector machines. The impact of different preprocessing choices is assessed on a real world dataset from direct marketing using a multifactorial analysis of variance on various performance metrics and method parameterisations. Our case-based analysis provides empirical evidence that data preprocessing has a significant impact on predictive accuracy, with certain schemes proving inferior to competitive approaches. In addition, it is found that (1) selected methods prove almost as sensitive to different data representations as to method parameterisations, indicating the potential for increased performance through effective preprocessing; (2) the impact of preprocessing schemes varies by method, indicating different ‘best practice’ setups to facilitate superior results of a particular method; (3) algorithmic sensitivity towards preprocessing is consequently an important criterion in method evaluation and selection which needs to be considered together with traditional metrics of predictive power and computational efficiency in predictive data mining.
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paper_title: Forecasting with artificial neural networks : The state of the art *

paper_content:

Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous surge in research activities in the past decade. While ANNs provide a great deal of promise, they also embody much uncertainty. Researchers to date are still not certain about the effect of key factors on forecasting performance of ANNs. This paper presents a state-of-the-art survey of ANN applications in forecasting. Our purpose is to provide (1) a synthesis of published research in this area, (2) insights on ANN modeling issues, and (3) the future research directions. © 1998 Elsevier Science B.V.
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paper_title: Evaluating the performance of Cost-based Discretization versus Entropy-and Error-based Discretization

paper_content:

Discretization is defined as the process that divides continuous numeric values into intervals of discrete categorical values. In this article, the concept of cost-based discretization as a pre-processing step to the induction of a classifier is introduced in order to obtain an optimal multi-interval splitting for each numeric attribute. A transparent description of the method and the steps involved in cost-based discretization are given. The aim of this paper is to present this method and to assess the potential benefits of such an approach. Furthermore, its performance against two other well-known methods, i.e. entropy-and pure error-based discretization is examined. To this end, experiments on 14 data sets, taken from the UCI Repository on Machine Learning were carried out. In order to compare the different methods, the area under the Receiver Operating Characteristic (ROC) graph was used and tested on its level of significance. For most data sets the results show that cost-based discretization achieves satisfactory results when compared to entropy-and error-based discretization.Given its importance, many researchers have already contributed to the issue of discretization in the past. However, to the best of our knowledge, no efforts have been made yet to include the concept of misclassification costs to find an optimal multi-split for discretization purposes, prior to induction of the decision tree. For this reason, this new concept is introduced and explored in this article by means of operations research techniques.
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paper_title: On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities

paper_content:

Previous work on the solution to analytical electronic customer relationship management (eCRM) problems has used either data-mining (DM) or optimization methods, but has not combined the two approaches. By leveraging the strengths of both approaches, the eCRM problems of customer analysis, customer interactions, and the optimization of performance metrics (such as the lifetime value of a customer on the Web) can be better analyzed. In particular, many eCRM problems have been traditionally addressed using DM methods. There are opportunities for optimization to improve these methods, and this paper describes these opportunities. Further, an online appendix (mansci.pubs.informs.org/ecompanion.html) describes how DM methods can help optimization-based approaches. More generally, this paper argues that the reformulation of eCRM problems within this new framework of analysis can result in more powerful analytical approaches.
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paper_title: Statistical Pattern Recognition: A Review

paper_content:

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
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paper_title: Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning

paper_content:

Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a computerized forecasting system to produce initial forecasts and the subsequent judgmental adjustment of these forecasts by the company's demand planners, ostensibly to take into account exceptional circumstances expected over the planning horizon. Making these adjustments can involve considerable management effort and time, but do they improve accuracy, and are some types of adjustment more effective than others? To investigate this, we collected data on more than 60,000 forecasts and outcomes from four supply-chain companies. In three of the companies, on average, judgmental adjustments increased accuracy. However, a detailed analysis revealed that, while the relatively larger adjustments tended to lead to greater average improvements in accuracy, the smaller adjustments often damaged accuracy. In addition, positive adjustments, which involved adjusting the forecast upwards, were much less likely to improve accuracy than negative adjustments. They were also made in the wrong direction more frequently, suggesting a general bias towards optimism. Models were then developed to eradicate such biases. Based on both this statistical analysis and organisational observation, the paper goes on to analyse strategies designed to enhance the effectiveness of judgmental adjustments directly.
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paper_title: Expert Opinions in Forecasting: The Role of the Delphi Technique

paper_content:

Expert opinion is often necessary in forecasting tasks because of a lack of appropriate or available information for using statistical procedures. But how does one get the best forecast from experts? One solution is to use a structured group technique, such as Delphi, for eliciting and combining expert judgments. In using the Delphi technique, one controls the exchange of information between anonymous panelists over a number of rounds (iterations), taking the average of the estimates on the final round as the group judgment. A number of principles are developed here to indicate how to conduct structured groups to obtain good expert judgments. These principles, applied to the conduct of Delphi groups, indicate how many and what type of experts to use (five to 20 experts with disparate domain knowledge); how many rounds to use (generally two or three); what type of feedback to employ (average estimates plus justifications from each expert); how to summarize the final forecast (weight all experts’ estimates equally); how to word questions (in a balanced way with succinct definitions free of emotive terms and irrelevant information); and what response modes to use (frequencies rather than probabilities or odds, with coherence checks when feasible). Delphi groups are substantially more accurate than individual experts and traditional groups and somewhat more accurate than statistical groups (which are made up of noninteracting individuals whose judgments are aggregated). Studies support the advantage of Delphi groups over traditional groups by five to one with one tie, and their advantage over statistical groups by 12 to two with two ties. We anticipate that by following these principles, forecasters may be able to use structured groups to harness effectively expert opinion.
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paper_title: Improving Judgment in Forecasting

paper_content:

Principles designed to improve judgment in forecasting aim to minimize inconsistency and bias at different stages of the forecasting process (formulation of the forecasting problem, choice of method, application of method, comparison and combination of forecasts, assessment of uncertainty in forecasts, adjustment of forecasts, evaluation of forecasts). The seven principles discussed concern the value of checklists, the importance of establishing agreed criteria for selecting forecast methods, retention and use of forecast records to obtain feedback, use of graphical rather than tabular data displays, the advantages of fitting lines through graphical displays when making forecasts, the advisability of using multiple methods to assess uncertainty in forecasts, and the need to ensure that people assessing the chances of a plan’s success are different from those who develop and implement it.
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paper_title: Judgmental forecasting: A review of progress over the last 25 years

paper_content:

The past 25 years has seen phenomenal growth of interest in judgemental approaches to forecasting and a significant change of attitude on the part of researchers to the role of judgement. While previously judgement was thought to be the enemy of accuracy, today judgement is recognised as an indispensable component of forecasting and much research attention has been directed at understanding and improving its use. Human judgement can be demonstrated to provide a significant benefit to forecasting accuracy but it can also be subject to many biases. Much of the research has been directed at understanding and managing these strengths and weaknesses. An indication of the explosion of research interest in this area can be gauged by the fact that over 200 studies are referenced in this review.
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paper_title: Principles Of Forecasting A Handbook For Researchers And Practitioners

paper_content:

Thank you very much for downloading principles of forecasting a handbook for researchers and practitioners. As you may know, people have look numerous times for their chosen books like this principles of forecasting a handbook for researchers and practitioners, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their laptop.
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paper_title: Economic Forecast Evaluation: Profits versus the Conventional Error Measures

paper_content:

Economists are often puzzled as to why profit-maximizing firms buy professional forecasts when statistics such as the root-mean-squared error or the mean absolute error often indicate that a naive model will forecast about as well. This paper argues that the reason is that these traditional summary statistics may not be closely related to a forecast's profits. Using profit measures, the authors find only very weak relationships between such summary error statistics and forecast value. If these results are robust, then least-squares regression analysis may not be appropriate for many studies of economic behavior. Copyright 1991 by American Economic Association.
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paper_title: Forecasting daily supermarket sales using exponentially weighted quantile regression

paper_content:

Inventory control systems typically require the frequent updating of forecasts for many different products. In addition to point predictions, interval forecasts are needed to set appropriate levels of safety stock. The series considered in this paper are characterised by high volatility and skewness, which are both time-varying. These features motivate the consideration of forecasting methods that are robust with regard to distributional assumptions. The widespread use of exponential smoothing for point forecasting in inventory control motivates the development of the approach for interval forecasting. In this paper, we construct interval forecasts from quantile predictions generated using exponentially weighted quantile regression. The approach amounts to exponential smoothing of the cumulative distribution function, and can be viewed as an extension of generalised exponential smoothing to quantile forecasting. Empirical results are encouraging, with improvements over traditional methods being particularly apparent when the approach is used as the basis for robust point forecasting.
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paper_title: Evaluating forecast performance in an inventory control system

paper_content:

This paper analyzes the impact of forecasting on inventory decisions in a large physical distribution system. Alternative forecasting models are evaluated by developing tradeoff curves between inventory investment and customer service. The results demonstrate that the choice of forecasting model is an important factor in determining the amount of investment needed to support any target level of customer service.
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paper_title: Density Forecasting: A Survey

paper_content:

A density forecast of the realization of a random variable at some future time is an estimate of the probability distribution of the possible future values of that variable. This article presents a selective survey of applications of density forecasting in macroeconomics and finance, and discusses some issues concerning the production, presentation and evaluation of density forecasts.
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paper_title: Bootstrapping Prediction Intervals for Autoregressive Models

paper_content:

The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on the bootstrap is considered. Three methods are considered for countering the small-sample bias of least-squares estimation for processes which have roots close to the unit circle: a bootstrap bias-corrected OLS estimator; the use of the Roy-Fuller estimator in place of OLS; and the use of the Andrews-Chen estimator in place of OLS. All three methods of bias correction yield superior results to the bootstrap in the absence of bias correction. Of the three correction methods, the bootstrap prediction intervals based on the Roy-Fuller estimator are generally superior to the other two. The small-sample performance of bootstrap prediction intervals based on the Roy-Fuller estimator are investigated when the order of the AR model is unknown, and has to be determined using an information criterion.
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paper_title: Another look at measures of forecast accuracy

paper_content:

We discuss and compare measures of accuracy of univariate time series forecasts. The methods used in the M-competition and the M3-competition, and many of the measures recommended by previous authors on this topic, are found to be inadequate, and many of them are degenerate in commonly occurring situations. Instead, we propose that the mean absolute scaled error become the standard measure for comparing forecast accuracy across multiple time series.
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paper_title: Prediction Intervals for Time-Series Forecasting

paper_content:

Computing prediction intervals (PIs) is an important part of the forecasting process intended to indicate the likely uncertainty in point forecasts. The commonest method of calculating PIs is to use theoretical formulae conditional on a best-fitting model. If a normality assumption is used, it needs to be checked. Alternative computational procedures that are not so dependent on a fitted model include the use of empirically based and re-sampling methods. Some so-called approximate formulae should be avoided. PIs tend to be too narrow because out-of-sample forecast accuracy is often poorer than would be expected from within-sample fit, particularly for PIs calculated conditional on a model fitted to past data. Reasons for this include uncertainty about the model and a changing environment. Ways of overcoming these problems include using a mixture of models with a Bayesian approach and using a forecasting method that is designed to be robust to changes in the underlying model.
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paper_title: The evaluation of extrapolative forecasting methods

paper_content:

Extrapolative forecasting methods are widely used in production and inventory decisions. Typically many hundreds of series are forecast and the cost-effectiveness of the decisions depends on the accuracy of the forecasting method(s) used. This paper examines how a forecasting method should be chosen based on analyzing alternative loss functions. It is argued that a population of time series must be evaluated by time period and by series. Of the alternative loss functions considered, only the geometric root mean squared error is well-behaved and has a straightforward interpretation. The paper concludes that exponential smoothing and ‘naive’ models, previously thought to be ‘robust’ performers, forecast poorly for the particular set of time series under analysis, whatever error measure is used. As a consequence, forecasters should carry out a detailed evaluation of the data series, as described in the paper, rather than relying on a priori analysis developed from earlier forecasting competitions.
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paper_title: Density Forecasting for Weather Derivative Pricing

paper_content:

Weather derivatives enable energy companies to protect themselves against weather risk. Weather ensemble predictions are generated from atmospheric models and consist of multiple future scenarios for a weather variable. They can be used to forecast the density of the payoff from a weather derivative. The mean of the density is the fair price of the derivative, and the distribution about the mean is important for risk management tools, such as value-at-risk models. In this empirical paper, we use 1- to 10-day-ahead temperature ensemble predictions to forecast the mean and quantiles of the density of the payoff from a 10-day heating degree day put option. The ensemble-based forecasts compare favourably with those based on a univariate time series GARCH model. Promising quantile forecasts are also produced using quantile autoregression to model the forecast error of an ensemble-based forecast for the expected payoff.
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paper_title: Error Measures For Generalizing About Forecasting Methods: Empirical Comparisons

paper_content:

This study evaluated measures for making comparisons of errors across time series. We analyzed 90 annual and 101 quarterly economic time series. We judged error measures on reliability, construct validity, sensitivity to small changes, protection against outliers, and their relationship to decision making. The results lead us to recommend the Geometric Mean of the Relative Absolute Error (GMRAE) when the task involves calibrating a model for a set of time series. The GMRAE compares the absolute error of a given method to that from the random walk forecast. For selecting the most accurate methods, we recommend the Median RAE (MdRAE) when few series are available and the Median Absolute Percentage Error (MdAPE) otherwise. The Root Mean Square Error (RMSE) is not reliable, and is therefore inappropriate for comparing accuracy across series.
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paper_title: Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting

paper_content:

Accurate forecasts are crucial to successful organizational planning. In 2001, 40 international experts published a set of principles to guide best practices in forecasting. Some of these principles relate to the use of management judgment. Most organizations use judgment at some stage in their forecasting process, but do they do so effectively? Although judgment can lead to significant improvements in forecasting accuracy, it can also be biased and inconsistent. The principles show how forecasters should use judgment and assess its effectiveness. We conducted a survey of 149 forecasters to examine the use of judgment based on these established principles and to investigate whether their forecasting procedures were consistent with the principles. In addition, we conducted four in-depth case studies. Although we found examples of good practice, we also discovered that many organizations would improve forecast accuracy if they followed basic principles such as limiting judgmental adjustments of quantitative forecasts, requiring managers to justify their adjustments in writing, and assessing the results of judgmental interventions.
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paper_title: Quantitative Forecasting — The State of the Art: Extrapolative Models

paper_content:

This paper sponsored by forecasting Study Group of the O.R. Society surveys the alternative extrapolative forecasting models regularly used and examines the various problems that arise. Extrapolative models are particularly appropriate when used for inventory control and materials purchase, and these two situations are considered. The paper concludes with an analysis of the evidence on the forecasting accuracy of extrapolative models.
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paper_title: Forecasting and Stock Control for Intermittent Demands

paper_content:

Exponential smoothing is frequently used for the forecasts in stock control systems. The analysis given shows that intermittent demands almost always produce inappropriate stock levels. Demand for constant quantities at fixed intervals may generate stock levels of up to double the quantity really needed. A method of overcoming these difficulties is described, using separate estimates of the size of demand, and of the demand frequency. The rules for setting the safety stock levels have also to be adjusted before consistent protection can be obtained against being out of stock.
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paper_title: Quantitative Forecasting — The State of the Art: Extrapolative Models

paper_content:

This paper sponsored by forecasting Study Group of the O.R. Society surveys the alternative extrapolative forecasting models regularly used and examines the various problems that arise. Extrapolative models are particularly appropriate when used for inventory control and materials purchase, and these two situations are considered. The paper concludes with an analysis of the evidence on the forecasting accuracy of extrapolative models.
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paper_title: Generalising about univariate forecasting methods: further empirical evidence

paper_content:

Abstract This paper extends the empirical evidence on the forecasting accuracy of extrapolative methods. The robustness of the major conclusions of the M-Competition data is examined in the context of the telecommunications data of Fildes (1992) . The performance of Robust Trend, found to be a successful method for forecasting the telecommunications data by Fildes, is compared with that of other successful methods using the M-Competition data. Although it is established that the structure of the telecommunications data is more homogeneous than that of the M-Competition data, the major conclusions of the M-Competition continue to hold for this new data set. In addition, while the Robust Trend method is confirmed to be the best performing method for the telecommunications data, for the 1001 M-Competition series, this method is outperformed by methods such as Single or Damped Smoothing. The performance of smoothing methods depended on how the smoothing parameters are estimated. Optimisation at each time origin was more accurate than optimisation at the first time origin, which in turn is shown to be superior to arbitrary (literature based) fixed values. In contrast to the last point, a data based choice of fixed smoothing constants from a cross-sectional study of the time series was found to perform well.
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paper_title: One day ahead demand forecasting in the utility industries: Two case studies

paper_content:

This paper describes two case studies of short-term demand forecasting for the utilities, water and gas, linked to earlier research in similar contexts. In both cases the forecast of demand has important consequences for the operations and control of productive capacity. It is shown that in these two cases extrapolative methods based on the past data history alone are outperformed by more complex multivariate approaches that include information on the effects of weather. The paper concludes with a discussion of how an organization with an important short-term forecasting problem should go about selecting an appropriate forecasting method.
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paper_title: Evidence for the selection of forecasting methods

paper_content:

Reid (1972) was among the first to argue that the relative accuracy of forecasting methods changes according to the properties of the time series. Comparative analyses of forecasting performance such as the M-Competition tend to support this argument. The issue addressed here is the usefulness of statistics summarizing the data available in a time series in predicting the relative accuracy of different forecasting methods. Nine forecasting methods are described and the literature suggesting summary statistics for choice of forecasting method is summarized. Based on this literature and further argument a set of these statistics is proposed for the analysis. These statistics are used as explanatory variables in predicting the relative performance of the nine methods using a set of simulated time series with known properties. These results are evaluated on observed data sets, the M-Competition data and Fildes Telecommunications data. The general conclusion is that the summary statistics can be used to select a good forecasting method (or set of methods) but not necessarily the best. Copyright © 2000 John Wiley & Sons, Ltd.
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paper_title: Evaluation of Aggregate and Individual Forecast Method Selection Rules

paper_content:

A major use of univariate forecasting methods lies in production control where there is a large number of series to be forecast. The appropriate choice of forecasting method has the potential for major cost savings through improved accuracy. Where a new method is to be compared to one already established two distinct approaches to selecting between the two can be considered: aggregate selection, where a single method is applied to all the time series, versus individual selection, where the particular method appropriate for each series is identified and used to forecast future observations for that series. This paper evaluates these two selection rules using 263 data series from a single organization. The results show the potential of "individual selection" and also the difficulty of attaining it. For short lead times "aggregate selection" achieves similar accuracy. For longer leads it is outperformed by "individual selection" and also undermined by sampling variability. As a consequence, "aggregate selection" must be carried out across a wide cross-section of series and across time. When this is done the results of this case study show that "aggregate selection" is both simpler than and of comparable accuracy to "individual selection."
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paper_title: Exponential smoothing in the telecommunications data

paper_content:

Exponential smoothing methods gave poor forecast accuracy in Fildes et al.'s study of telecommunications time series. We reexamine this study and show that the accuracy of the Holt and damped trend methods can be improved by trimming the time series to eliminate irrelevant early data, fitting the methods to minimize the MAD rather than the MSE, and optimizing the parameters. Contrary to Fildes et al., we show that the damped trend is more accurate than Holt's method. Because most of the telecommunications series display steady trends, we test the Theta method of forecasting and a closely related method, simple exponential smoothing with drift. The Theta method proves disappointing, but simple exponential smoothing with drift is the best smoothing method for this data, giving about the same accuracy as the robust trend.
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paper_title: Principles Of Forecasting A Handbook For Researchers And Practitioners

paper_content:

Thank you very much for downloading principles of forecasting a handbook for researchers and practitioners. As you may know, people have look numerous times for their chosen books like this principles of forecasting a handbook for researchers and practitioners, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their laptop.
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paper_title: Selecting the best periodic inventory control and demand forecasting methods for low demand items

paper_content:

The (s,S) form of the periodic review inventory control system has been claimed theoretically to be the best for the management of items of low and intermittent demand. Various heuristic procedures have been put forward, usually justified on the basis of generated data with known properties. Some stock controllers also have other simple rules which they employ and which are rarely seen in the literature. Determining how to forecast future demands is also a major problem in the area. The research described in this paper compares various periodic inventory policies as well as some forecasting methods and attempts to determine which are best for low and intermittent demand items. It evaluates the alternative methods on some long series of daily demands for low demand items for a typical spare parts depot.
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paper_title: Forecasting and Stock Control for Intermittent Demands

paper_content:

Exponential smoothing is frequently used for the forecasts in stock control systems. The analysis given shows that intermittent demands almost always produce inappropriate stock levels. Demand for constant quantities at fixed intervals may generate stock levels of up to double the quantity really needed. A method of overcoming these difficulties is described, using separate estimates of the size of demand, and of the demand frequency. The rules for setting the safety stock levels have also to be adjusted before consistent protection can be obtained against being out of stock.
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paper_title: Classification for forecasting and stock control:a case study

paper_content:

Different stock keeping units (SKUs) are associated with different underlying demand structures, which in turn require different methods for forecasting and stock control. Consequently, there is a need to categorize SKUs and apply the most appropriate methods in each category. The way this task is performed has significant implications in terms of stock and customer satisfaction. Therefore, categorization rules constitute a vital element of intelligent inventory management systems. Very little work has been conducted in this area and, from the limited research to date, it is not clear how managers should classify demand patterns for forecasting and inventory management. A previous research project was concerned with the development of a theoretically coherent demand categorization scheme for forecasting only. In this paper, the stock control implications of such an approach are assessed by experimentation on an inventory system developed by a UK-based software manufacturer. The experimental database consists of the individual demand histories of almost 16 000 SKUs. The empirical results from this study demonstrate considerable scope for improving real-world systems.
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paper_title: Forecasting for inventory control with exponential smoothing

paper_content:

Exponential smoothing, often used for sales forecasting in inventory control, has always been rationalized in terms of statistical models that possess errors with constant variances. It is shown in this paper that exponential smoothing remains the appropriate approach under more general conditions where the variances are allowed to grow and contract with corresponding movements in the underlying level. The implications for estimation and prediction are explored. In particular the problem of finding the prediction distribution of aggregate lead- time demand for use in inventory control calculations is considered. It is found that unless a drift term is added to simple exponential smoothing, the prediction distribution is largely unaffected by the variance assumption. A method for establishing order-up-to levels and reorder levels directly from the simulated prediction distributions is also proposed.
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paper_title: Forecasting for intermittent demand : the estimation of an unbiased average

paper_content:

The majority of the range of items held by many stockists exhibit intermittent demand. Accurate forecasting of the issue rate for such items is important and several methods have been developed, but all produce biased forecasts to a greater or lesser degree. This paper derives the bias expected when the order arrivals follows a Poisson process, which leads to a correction factor for application in practice. Extensions to some other arrival processes are briefly considered.
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paper_title: A new approach to forecasting intermittent demand for service parts inventories

paper_content:

Abstract A fundamental aspect of supply chain management is accurate demand forecasting. We address the problem of forecasting intermittent (or irregular) demand, i.e. random demand with a large proportion of zero values. This pattern is characteristic of demand for service parts inventories and capital goods and is difficult to predict. We forecast the cumulative distribution of demand over a fixed lead time using a new type of time series bootstrap. To assess accuracy in forecasting an entire distribution, we adapt the probability integral transformation to intermittent demand. Using nine large industrial datasets, we show that the bootstrapping method produces more accurate forecasts of the distribution of demand over a fixed lead time than do exponential smoothing and Croston’s method.
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paper_title: Forecasting for the ordering and stock-holding of spare parts

paper_content:

A modern military organization like the UK's Royal Air Force is dependent on readily available spare parts for in-service aircraft in order to maximize operational capability. A large proportion of spare parts are known to have an intermittent or slow-moving demand pattern, presenting particular problems as far as forecasting and inventory control are concerned. In this paper, we use extensive demand and replenishment lead-time data to assess the practical value of forecasting models put forward in the literature for addressing these problems. We use an analytical method for classifying the consumable inventory into smooth, irregular, slow-moving and intermittent demand patterns. Recent forecasting developments are compared against more commonly used methods across the identified demand patterns. One recently developed method, a modification to Croston's method referred to as the approximation method, is observed to provide significant reductions in the value of the stock-holdings required to attain a specified service level for all demand patterns.
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paper_title: Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method

paper_content:

Abstract Intermittent demand appears at random, with many time periods having no demand. Manufacturers perceive the forecasting of intermittent data to be an important problem. In practice, the standard method of forecasting intermittent demand is single exponential smoothing, although some production management texts suggest the lesser-known alternative of Croston's method [Croston J.D., 1972, Forecasting and stock control for intermittent demands, Operational Research Quarterly , 23(3), 289-303]. We compared the two methods, using artificial data created to violate Croston's assumptions and real-world data from industrial sources. We conclude that Croston's method is robustly superior to exponential smoothing and could provide tangible benefits to manufacturers forecasting intermittent demand.
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paper_title: On the stock control performance of intermittent demand estimators

paper_content:

The purpose of this paper is to assess the empirical stock control performance of intermittent demand estimation procedures. The forecasting methods considered are the simple moving average, single exponential smoothing, Croston's method and a new method recently developed by the authors of this paper. We first discuss the nature of the empirical demand data set (3000 stock keeping units) and we specify the stock control model to be used for experimentation purposes. Performance measures are then selected to report customer service level and stock volume differences. The out-of-sample empirical comparison results demonstrate the superior stock control performance of the new intermittent demand forecasting method and enable insights to be gained into the empirical utility of the other estimators.
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paper_title: Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning

paper_content:

Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a computerized forecasting system to produce initial forecasts and the subsequent judgmental adjustment of these forecasts by the company's demand planners, ostensibly to take into account exceptional circumstances expected over the planning horizon. Making these adjustments can involve considerable management effort and time, but do they improve accuracy, and are some types of adjustment more effective than others? To investigate this, we collected data on more than 60,000 forecasts and outcomes from four supply-chain companies. In three of the companies, on average, judgmental adjustments increased accuracy. However, a detailed analysis revealed that, while the relatively larger adjustments tended to lead to greater average improvements in accuracy, the smaller adjustments often damaged accuracy. In addition, positive adjustments, which involved adjusting the forecast upwards, were much less likely to improve accuracy than negative adjustments. They were also made in the wrong direction more frequently, suggesting a general bias towards optimism. Models were then developed to eradicate such biases. Based on both this statistical analysis and organisational observation, the paper goes on to analyse strategies designed to enhance the effectiveness of judgmental adjustments directly.
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paper_title: Operations research and data mining

paper_content:

With the rapid growth of databases in many modern enterprises data mining has become an increasingly important approach for data analysis. The operations research community has contributed significantly to this field, especially through the formulation and solution of numerous data mining problems as optimization problems, and several operations research applications can also be addressed using data mining methods. This paper provides a survey of the intersection of operations research and data mining. The primary goals of the paper are to illustrate the range of interactions between the two fields, present some detailed examples of important research work, and provide comprehensive references to other important work in the area. The paper thus looks at both the different optimization methods that can be used for data mining, as well as the data mining process itself and how operations research methods can be used in almost every step of this process. Promising directions for future research are also identified throughout the paper. Finally, the paper looks at some applications related to the area of management of electronic services, namely customer relationship management and personalization.
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paper_title: The Value of Information Sharing in a Two-Level Supply Chain

paper_content:

Many companies have embarked on initiatives that enable more demand information sharing between retailers and their upstream suppliers. While the literature on such initiatives in the business press is proliferating, it is not clear how one can quantify the benefits of these initiatives and how one can identify the drivers of the magnitudes of these benefits. Using analytical models, this paper aims at addressing these questions for a simple two-level supply chain with nonstationary end demands. Our analysis suggests that the value of demand information sharing can be quite high, especially when demands are significantly correlated over time.
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paper_title: Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times and Information

paper_content:

An important observation in supply chain management, known as the bullwhip effect, suggests that demand variability increases as one moves up a supply chain. In this paper we quantify this effect for simple, two-stage supply chains consisting of a single retailer and a single manufacturer. Our model includes two of the factors commonly assumed to cause the bullwhip effect: demand forecasting and order lead times. We extend these results to multiple-stage supply chains with and without centralized customer demand information and demonstrate that the bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information.
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paper_title: Gaining Benefits from Joint Forecasting and Replenishment Processes: The Case of Auto-Correlated Demand

paper_content:

In this paper we consider a cooperative, two-level supply chain consisting of a retailer and a supplier. As in many practical settings, the supply chain members progressively observe market signals that enable them to explain future demand. The demand itself evolves according to an auto-regressive time series. We examine three types of supply chain configurations. In the first setting, the retailer and the supplier coordinate their policy parameters in an attempt to minimize systemwide costs, but they do not share their observations of market signals. In the second setting, resembling many original vendor-managed inventory (VMI) programs, the supplier takes the full responsibility of managing the supply chain's inventory, but the retailer's observations of market signals are not transferred to him. In our third setting, reminiscent of collaborative forecasting and replenishment partnerships, inventory is managed centrally, and all demand related information is shared. We propose a set of stylized models to study the three settings and use them to provide managerial insights into the value of information sharing, VMI, and collaborative forecasting.
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paper_title: Modelling the benefits of information sharing-based partnerships in a two-level supply chain

paper_content:

This paper presents a study aimed at quantifying the benefits of information sharing-based supply chain partnerships. We derive the optimal inventory policies for the manufacturer and the retailer in a two-level decentralized supply chain under different information sharing scenarios. We show that increasing information sharing among the members in a decentralized supply chain will lead to Pareto improvement in the performance of the entire chain. Specifically, the manufacturer can obtain benefits in terms of reductions in inventory levels and cost savings.
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paper_title: The Effect of Collaborative Forecasting on Supply Chain Performance

paper_content:

We consider a cooperative, two-stage supply chain consisting of two members: a retailer and a supplier. In our first model, called local forecasting, each member updates the forecasts of future demands periodically, and is able to integrate the adjusted forecasts into his replenishment process. Forecast adjustments made at both levels of the supply chain can be correlated. The supply chain has a decentralized information structure, so that day-to-day inventory and forecast information are known locally only. In our second model, named collaborative forecasting, the supply chain members jointly maintain and update a single forecasting process in the system. Hence, forecasting information becomes centralized. Finally, we consider as a benchmark the special case in which forecasts are not integrated into the replenishment processes at all. We propose a unified framework that allows us to study and compare the three types of settings. This study comes at a time when various types of collaborative forecasting partnerships are being experimented within industry, and when the drivers for success or failure of such initiatives are not yet fully understood. In addition to providing some managerial insights into questions that arise in this context, our set of models is tailored to serve as building blocks for future work in this emerging area of research.
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paper_title: Forecasting collaboration in the European grocery sector: Observations from a case study

paper_content:

Abstract Modeling-based research provides strong support for collaborative forecasting as a means of improving supply chain efficiency. Yet, despite positive attitudes towards collaboration and several pilot implementations undertaken in the beginning of the millennium, large-scale implementations of collaborative forecasting are still scarce. This article presents the results of an exploratory case study examining four collaboration projects involving four manufacturers and one retailer operating in the European grocery sector. By analyzing the positive and negative experiences of these companies and the results of their collaboration projects, factors that have an impact on the feasibility and value of forecasting collaboration are identified. A main finding is that many collaboration models appear to build on invalid assumptions concerning retailers’ forecasting needs, resources, and processes, as well as manufacturers’ capabilities to benefit from the demand or forecast information made available through collaboration.
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paper_title: COST INCREASES DUE TO DEMAND UNCERTAINTY IN MRP LOT SIZING

paper_content:

This paper analyzes the cost increases due to demand uncertainty in single-level MRP lot sizing on a rolling horizon. It is shown that forecast errors have a tremendous effect on the cost effectiveness of lot-sizing techniques even when these forecast errors are small. Moreover, the cost differences between different techniques become rather insignificant in the presence of forecast errors. Since most industrial firms face demand uncertainty to some extent, our findings may have important managerial implications. Various simulation experiments give insight into both the nature and the magnitude of the cost increases for different heuristics. Analytical results are developed for the constant-demand case with random noise and forecasting by exponential smoothing. It is also shown how optimal buffers can be obtained by use of a simple model. Although the analysis in this paper is restricted to simplified cases, the results merit further consideration and study. This paper is one of the first to inject forecast errors into MRP lot-sizing research. As such it attempts to deal with one of the major objections against the practical relevance of previous research in this area.
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paper_title: The impact of forecasting model selection on the value of information sharing in a supply chain

paper_content:

This paper presents a study on the impact of forecasting model selection on the value of information sharing in a supply chain with one capacitated supplier and multiple retailers. Using a computer simulation model, this study examines demand forecasting and inventory replenishment decisions by the retailers, and production decisions by the supplier under different demand patterns and capacity tightness. Analyses of the simulation output indicate that the selection of the forecasting model significantly influences the performance of the supply chain and the value of information sharing. Furthermore, demand patterns faced by retailers and capacity tightness faced by the supplier also significantly influence the value of information sharing. The result also shows that substantial cost savings can be realized through information sharing and thus help to motivate trading partners to share information in the supply chain. The findings can also help supply chain managers select suitable forecasting models to improve supply chain performance.
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paper_title: Market response models : econometric and time-series analysis

paper_content:

Preface. I: Introduction. 1. Response Models for Marketing Management. 2. Markets, Data, and Sales Drivers. II: Market Response in Stationary Markets. 3. Design of Static Response Models. 4. Design of Dynamic Response Models. 5. Parameter Estimation and Model Testing. III: Market Response in Evolving Markets. 6. Single Marketing Time Series. 7. Multiple Marketing Time Series. IV: Solving Marketing Problems with ETS. 8. Empirical Findings and Managerial Insights. 9. Making Marketing Plans and Sales Forecasts. V: Conclusion. 10. Implementation. Bibliography. Author Index. Company/Brand Index. Industry/Category/Product Index. Subject Index.
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paper_title: Forecasting Methods for Marketing: Review of Empirical Research

paper_content:

This paper reviews the empirical research on forecasting in marketing. In addition, it presents results from some small scale surveys. We offer a framework for discussing forecasts in the area of marketing, and then review the literature in light of that framework. Particular emphasis is given to a pragmatic interpretation of the literature and findings. Suggestions are made on what research is needed.
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paper_title: Prelaunch forecasting of new automobiles

paper_content:

This paper develops and applies a prelaunch model and measurement system to the marketing planning of a new automobile. The analysis addresses active search by consumers, dealer visits, word-of-mouth communication, magazine reviews, and production constraints---issues that are important in understanding consumer response to durable goods. We address these issues with a detailed consumer flow model which monitors and projects key consumer transitions in response to marketing actions. A test-vs.-control consumer clinic provides data which, with judgment and previous experience, are used to "calibrate" the model to fit the sales history of the control car. We illustrate how the model evolved to meet management needs and provided suggestions on advertising, dealer training, and consumer incentives. Comparison of the model's predictions to actual sales data suggests reasonable accuracy when an implemented strategy matches the planned strategy.
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paper_title: Telecommunications Demand Forecasting - A Review

paper_content:

Abstract The last decade has seen rapid advances in telecommunications technology in an increasingly deregulated and competitive market place. Companies operating in these various markets have relied on demand forecasts to justify the considerable investment needed to ensure capacity availability at the right time. These new markets are typically composed of new consumers taking up a product or service for the first time, established users changing their usage patterns, users of competing services shifting to the alternative service and those exiting from this segment of the market altogether. This paper describes various models that have been used to understand market dynamics. Markets discussed include both established and new: mobile, the internet, and PSTN (public switched telephony network). Cross-sectional choice models of the mode of accessing the service are discussed along with models for usage in established markets. These models typically include price (and perceived price) differentials and use standard econometric methods, focusing on price elasticity estimation. Forecasting accuracy has been neglected. New product models may include additional ‘drivers’ such as aspects of service quality and the attributes of the products themselves. Both choice models of adoption of new products and Bass-type diffusion models have been used in forecasting. Because of the complexity of the ‘drivers’ of the adoption process, the successful modelling of these new markets has been limited, not least by inadequate data. Simulation models have been proposed to structure the problem more completely and overcome these inadequacies. Both these classes of model have not been effectively validated, researchers having been content just to propose a new approach without thoroughly testing it against alternatives. The only class of telecommunications forecasting problem that has been more thoroughly analysed are those needed to support operations such as call centres. This review paper describes the research that has been carried out on the three problem areas of established products, new products and operations, highlighting areas where further research is needed. The paper also serves as an introduction to the Special Issue on Telecoms Forecasting by describing how the papers contribute to the developing research agenda.
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paper_title: A Meta-Analysis of Applications of Diffusion Models

paper_content:

A meta-analysis of 213 applications of diffusion models from 15 articles relates model parameters to the nature of the innovation, the country under study, model specification, and estimation proce...
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paper_title: Technological Forecasting-Model Selection, Model Stability, and Combining Models

paper_content:

The paper identifies 29 models that the literature suggests are appropriate for technological forecasting. These models are divided into three classes according to the timing of the point of inflexion in the innovation or substitution process. Faced with a given data set and such a choice, the issue of model selection needs to be addressed. Evidence used to aid model selection is drawn from measures of model fit and model stability. An analysis of the forecasting performance of these models using simulated data sets shows that it is easier to identify a class of possible models rather than the "best" model. This leads to the combining of model forecasts. The performance of the combined forecasts appears promising with a tendency to outperform the individual component models.
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paper_title: Forecasting S-shaped diffusion processes via response modelling methodology

paper_content:

Diffusion processes abound in various areas of corporate activities, such as the time-dependent behaviour of cumulative demand of a new product, or the adoption rate of a technological innovation. In most cases, the proportion of the population that has adopted the new product by time t behaves like an S-shaped curve, which resembles the sigmoid curve typical to many known statistical distribution functions. This analogy has motivated the common use of the latter for forecasting purposes. Recently, a new methodology for empirical modelling has been developed, termed response modelling methodology (RMM). The error distribution of the RMM model has been shown to model well variously shaped distribution functions, and may therefore be adequate to forecast sigmoid-curve processes. In particular, RMM may be applied to forecast S-shaped diffusion processes. In this paper, forty-seven data sets, assembled from published sources by Meade and Islam, are used to compare the accuracy and the stability of RMM-generated forecasts, relative to current commonly applied models. Results show that in most comparisons RMM forecasts outperform those based on any individually selected distributional model.
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paper_title: A unified view of statistical forecasting procedures

paper_content:

A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods, such as the exponentially weighted moving average, to more complex procedures such as Box–Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper sets out to show the relationship between these various procedures by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components. The framework is then extended to cover models with explanatory variables. From the technical point of view the Kalman filter plays an important role in allowing an integrated treatment of these topics.
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paper_title: Forecasting with Conjoint Analysis

paper_content:

Conjoint analysis is a survey-based method managers often use to obtain consumer input to guide their new-product decisions. The commercial popularity of the method suggests that conjoint results improve the quality of those decisions. We discuss the basic elements of conjoint analysis, describe conditions under which the method should work well, and identify difficulties with forecasting marketplace behavior. We introduce one forecasting principle that establishes the forecast accuracy of new-product performance in the marketplace. However, practical complexities make it very difficult for researchers to obtain incontrovertible evidence about the external validity of conjoint results. Since published studies typically rely on holdout tasks to compare the predictive validities of alternative conjoint procedures, we describe the characteristics of such tasks, and discuss the linkages to conjoint data and marketplace choices. We then introduce five other principles that can guide conjoint studies to enhance forecast accuracy.
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paper_title: A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures

paper_content:

The primary objective of this paper is to develop a parsimonious model for forecasting the gross box-office revenues of new motion pictures based on early box office data. The paper also seeks to provide insights into the impact of distribution policies on the adoption of new products. The model is intended to assist motion picture exhibitor chains retailers in managing their exhibition capacity and in negotiating exhibition license agreements with distributors studios, by allowing them to project the box-office potential of the movies they plan to or currently exhibit based on early box-office results. It is also of interest to practitioners in other software industries e.g., music, books, CD-ROMs where the distribution intensity is highly variable over the product life cycle and is an important determinant of new product adoption patterns. The model and its extensions are of interest to academic researchers interested in modeling distribution effects in new product adoption, as well as forecasters looking for ways to leverage historical data on related products to forecast the sales of new products. ::: ::: We draw upon a queuing theory framework to conceptualize stochastically the consumer's movie adoption process in two steps---the time to decide to see the new movie, and the time to act on the adoption decision. The parameter for the time-to-decide process captures the intensity of information intensity flowing from various information sources, while the parameter for the time-to-act process is related to the delay created by limited distribution intensity and other factors. Our conceptualization extends existing new product forecasting models, which assume that consumers act instantaneously on the motivating information they receive about the new product. The resulting model is parsimonious, yet it accommodates a wide range of adoption patterns. In addition, the stochastic formulation allows us to quantify the uncertainty surrounding the expected adoption pattern. In the empirical testing, we focus on the most parsimonious version of the modeling framework. BOXMOD-I, a model that assumes stationarity with respect to the two shape parameters that characterize the adoption process. The model produces fairly accurate early forecasts using at most the first three weeks of data for calibration, and the predictive performance of the model compares favorably with benchmark models. We propose extensions of the basic model that account for more realistic non-stationary distribution intensity patterns---including a “wide release” pattern that relies on intensive distribution and promotion, and a “platform release” pattern that involves a gradual buildup of distribution intensity. Finally, we present an adaptive weighing scheme that combines initial parameter estimates obtained from a meta-analysis procedure with estimates obtained from early data to produce forecasts of box-office revenues for a new movie when little or no box-office data are available. ::: ::: An important finding from the empirical testing is that motion picture box-office revenue patterns display remarkable empirical regularity. We find that there are only three classes of adoption patterns, and these can all be represented within the basic model by using a two-parameter. Exponential or Erlang-2 probability distribution, or a three parameter Generalized Gamma distribution. We also find that cumulative box-office revenues can be predicted with reasonable accuracy often within 10% of the actual using as little as two or three data points. However, our attempts to predict revenue patterns without any sales data meet with limited success. While the scale parameter can be estimated reasonably well from a historical database of parameter values, we find that it is considerably more difficult to predict the shape parameters using historical data. ::: ::: The parsimony we seek in developing the model comes at the cost of several limiting assumptions. We assume that the time-to-decide subprocess and the time-to-act subprocess are independent, which may not be the case if decisions on continued exhibition by retailers are endogenously related to box-office revenues over the life cycle. In the basic model formulation, we also assume that the time-to-act process can be represented by an exponential distribution, which may not always be the case. While we provide some empirical evidence to support these assumptions, further research could relax these and other assumptions to enrich the basic model, although this would entail some loss in parsimony.
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paper_title: Modelling and forecasting the diffusion of innovation – A 25-year review

paper_content:

Abstract The wealth of research into modelling and forecasting the diffusion of innovations is impressive and confirms its continuing importance as a research topic. The main models of innovation diffusion were established by 1970. (Although the title implies that 1980 is the starting point of the review, we allowed ourselves to relax this constraint when necessary.) Modelling developments in the period 1970 onwards have been in modifying the existing models by adding greater flexibility in various ways. The objective here is to review the research in these different directions, with an emphasis on their contribution to improving on forecasting accuracy, or adding insight to the problem of forecasting. The main categories of these modifications are: the introduction of marketing variables in the parameterisation of the models; generalising the models to consider innovations at different stages of diffusions in different countries; and generalising the models to consider the diffusion of successive generations of technology. We find that, in terms of practical impact, the main application areas are the introduction of consumer durables and telecommunications. In spite of (or perhaps because of) the efforts of many authors, few research questions have been finally resolved. For example, although there is some convergence of ideas of the most appropriate way to include marketing mix-variables into the Bass model, there are several viable alternative models. Future directions of research are likely to include forecasting new product diffusion with little or no data, forecasting with multinational models, and forecasting with multi-generation models; work in normative modelling in this area has already been published.
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paper_title: Forecasting Using Growth Curves An Adaptive Approach

paper_content:

Growth curves such as the logistic and Gompertz are widely used for forecasting market development. The approach proposed is specifically designed for forecasting, rather than fitting available data—the usual approach with non-linear least squares regression. Two innovations form the foundation for this approach. The growth curves are reformulated from a time basis to an observation basis. This ensures that the available observations and the forecasts form a monotonic series; this is not necessarily true for least squares extrapolations of growth curves. An extension of the Kalman filter, an approach already used with linear forecasting models, is applied to the estimation of the growth curve coefficients. This allows the coefficients the flexibility to change over time if the market environment changes. The extended Kalman filter also proves the information for the generation of confidence intervals about the forecasts. Alternative forecasting approaches, least squares and an adaptive Bass model, suggested by Bretschneider and Mahajan, are used to produce comparative forecasts for a number of different data sets. The approach using the extended Kalman filter is shown to be more robust and almost always more accurate than the alternatives.
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paper_title: Principles Of Forecasting A Handbook For Researchers And Practitioners

paper_content:

Thank you very much for downloading principles of forecasting a handbook for researchers and practitioners. As you may know, people have look numerous times for their chosen books like this principles of forecasting a handbook for researchers and practitioners, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their laptop.
---
paper_title: Market response models : econometric and time-series analysis

paper_content:

Preface. I: Introduction. 1. Response Models for Marketing Management. 2. Markets, Data, and Sales Drivers. II: Market Response in Stationary Markets. 3. Design of Static Response Models. 4. Design of Dynamic Response Models. 5. Parameter Estimation and Model Testing. III: Market Response in Evolving Markets. 6. Single Marketing Time Series. 7. Multiple Marketing Time Series. IV: Solving Marketing Problems with ETS. 8. Empirical Findings and Managerial Insights. 9. Making Marketing Plans and Sales Forecasts. V: Conclusion. 10. Implementation. Bibliography. Author Index. Company/Brand Index. Industry/Category/Product Index. Subject Index.
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paper_title: Commercial Use of UPC Scanner Data: Industry and Academic Perspectives

paper_content:

The authors report the findings from an exploratory investigation of the use of UPC scanner data in the consumer packaged goods industry in the U.S. The study examines the practitioner community's view of the use of scanner data and compares these views with academic research. Forty-one executives from ten data suppliers, packaged goods manufacturers, and consulting firms participated in wide-ranging, in-person, interviews conducted by the authors. The interviews sought to uncover key questions practitioners would like to answer with scanner data, how scanner data is applied to these questions, and the industry's perspective regarding the success that the use of scanner data has had in each area. The authors then compare and contrast practitioners' views regarding the resolution of each issue with academic research. This produces a 2 × 2 classification of each question as "resolved" or "unresolved" from the perspectives of industry and academia. Along the diagonal of the 2 × 2, issues viewed as unresolved by both groups are important topics for future research. Issues deemed resolved by both groups are, correspondingly, of lower priority. In the off-diagonal cells, industry and academics disagree. These topics should be given priority for discussion, information exchange, and possible further research. Practitioners reported that scanner data analysis has had the most success and been most widely adopted for decision making in consumer promotions (i.e., coupons), trade promotions, and pricing. For example, logit and regression models applied to scanner data have revealed very low average consumer response to coupons which has directly led to reduced couponing activity. Managers also reported high levels of comfort with and impact from analyses of trade promotions and price elasticities. While industry views most of the issues in these areas to be resolved, academic research raises concerns about a number of practices in common commercial use. These include price threshold analysis and trade promotion evaluation using baseline and incremental sales. In product strategy, advertising, and distribution management, practitioners reported that the use of scanner data has had more limited development, success, and impact. In the case of new product decisions, scanner data use has been slow to develop due to the inherent limitations of historicaldata for these decisions and a heavy reliance on traditional primary research methods. In advertising, scanner data is widely analyzed with models, but confusion among practitioners is very high due to controversies about methods (e.g., what level of data aggregation is best) and conflicting results. In distribution and retail management, scanner data use has tremendous potential but a mixed track record to date. Thus, practitioners view the use of scanner data as unresolved for most issues in product strategy, advertising, and distribution. This view is largely, though not entirely, consistent with academic research, which has only begun to address many of the key questions raised by practitioners. In light of the large number of unresolved issues and mixed record of scanner data use to date, the authors offer aseries of specific recommendations for immediate and long-term research priorities that are likely to have the greatest impact on commercial utilization of UPC scanner data. Topics of immediate priority include price thresholds and gaps, baseline and incremental sales, base price elasticity, competitive reactions, measurement of advertising effects, management of brand equity, rationalization of product assortments, and category management. Long-term priorities include a greater emphasis on profitability versus sales or market share, developing prescriptive models versus descriptive models, and the need for industry standards.
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paper_title: The Success of Marketing Management Support Systems

paper_content:

This paper provides an introduction to this Special Issue by a providing a framework for evaluating the potential and actual success of marketing management support systems MMSS, and b briefly discussing how each paper in this Special Issue addresses the general topic of managerial decision making. The paper concludes by outlining some key questions that still need to be addressed.
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paper_title: Managerial Applications of Neural Networks: The Case of Bank Failure Predictions

paper_content:

This paper introduces a neural-net approach to perform discriminant analysis in business research. A neural net represents a nonlinear discriminant function as a pattern of connections between its processing units. Using bank default data, the neural-net approach is compared with linear classifier, logistic regression, kNN, and ID3. Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness. Limitations of using neural nets as a general modeling tool are also discussed.
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paper_title: A neural network application to consumer classification to improve the timing of direct marketing activities

paper_content:

This article develops an alternative estimation approach for classifying new prospective consumers as "good'' or "bad'' prospects for direct marketing purposes. We show that the traditional approach of using demographics alone to profile non-active consumers (those who have yet to buy in the category) can be improved by waiting to observe their initial and limited number of sequential purchases in the category. We call this method the early purchase classification (EPC) approach. We make use of two established classification models, a multinomial logit model (MNL) and a neural network model (NN), and show that the classification accuracy of both models using our EPC approach outperforms the traditional approach of classifying non-active prospects using demographics only. Furthermore, we find that the NN model consistently outperforms the MNL model at this task. This research uses the best aspects of each model by utilizing the MNL model to determine which variables are most relevant to the classification and then using those variables for classification in the NN model. Using the complementary features of the MNL and NN models, managers can use the EPC approach to determine the most profitable time in a purchasing history to classify and target prospective consumers new to their categories.
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paper_title: The direct marketing of insurance

paper_content:

An insurance company, like many in the financial services industry, will advertise a product in the press and some readers will avail themselves of it. Often the cost of an advertisement will exceed the income derived from the accepted respondents in the following years. It only becomes profitable if acquiring names into a database results in purchases in future years of the advertised and other, cross-sold products. Therefore evaluating advertising effectiveness requires the development of future lifetime values (LTVs), which vary over time between individuals for the different products. Real examples include evaluating the media vehicle, size, content and frequency of advertisements.
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paper_title: Knowledge Management and Data Mining for Marketing

paper_content:

Because of Multiplicity of the technology and information systems, the business is going to collect more customer data in large databases. However, the most useful insights into the characteristics of customer marketing and purchasing patterns are hidden. The recent emphasis on customer relationship management makes the performance of marketing very ideal application and by applying data mining tools for supporting decisions; it will get so many benefits. The systematic methodology that uses knowledge management and data mining is proposed for retrieve knowledge management and marketing decision. This methodology can be considered essential for enhancing customer relationship management.
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paper_title: Benchmarking state-of-the-art classification algorithms for credit scoring

paper_content:

In this paper, we study the performance of various state-of-the-art classification algorithms applied to eight real-life credit scoring data sets. Some of the data sets originate from major Benelux and UK financial institutions. Different types of classifiers are evaluated and compared. Besides the well-known classification algorithms (eg logistic regression, discriminant analysis, k-nearest neighbour, neural networks and decision trees), this study also investigates the suitability and performance of some recently proposed, advanced kernel-based classification algorithms such as support vector machines and least-squares support vector machines (LS-SVMs). The performance is assessed using the classification accuracy and the area under the receiver operating characteristic curve. Statistically significant performance differences are identified using the appropriate test statistics. It is found that both the LS-SVM and neural network classifiers yield a very good performance, but also simple classifiers such as logistic regression and linear discriminant analysis perform very well for credit scoring.
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paper_title: On the need for time series data mining benchmarks: a survey and empirical demonstration

paper_content:

In the last decade there has been an explosion of interest in mining time series data. Literally hundreds of papers have introduced new algorithms to index, classify, cluster and segment time series. In this work we make the following claim. Much of this work has very little utility because the contribution made (speed in the case of indexing, accuracy in the case of classification and clustering, model accuracy in the case of segmentation) offer an amount of “improvement” that would have been completely dwarfed by the variance that would have been observed by testing on many real world datasets, or the variance that would have been observed by changing minor (unstated) implementation details. ::: ::: To illustrate our point, we have undertaken the most exhaustive set of time series experiments ever attempted, re-implementing the contribution of more than two dozen papers, and testing them on 50 real world, highly diverse datasets. Our empirical results strongly support our assertion, and suggest the need for a set of time series benchmarks and more careful empirical evaluation in the data mining community.
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paper_title: Credit Scoring and its Applications

paper_content:

From the Publisher: ::: About the Author ::: ::: Lyn C. Thomas is a Professor of Management Science at the University of Southampton. Jonathan N. Crook is Reader in Business Economics at the University of Edinburgh. David B. Edelman is Credit Director of Royal Bank of Scotland, Edinburgh.
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paper_title: Neural Networks: A New Tool for Predicting Thrift Failures*

paper_content:

A neural network model that processes input data consisting of financial ratios is developed to predict the financial health of thrift institutions. The network's ability to discriminate between healthy and failed institutions is compared to a traditional statistical model. The differences and similarities in the two modelling approaches are discussed. The neural network, which uses the same financial data, requires fewer assumptions, achieves a higher degree of prediction accuracy, and is more robust.
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paper_title: From Data Mining to Knowledge Discovery in Databases

paper_content:

The data that most organizations possess is considered a valuable asset. To really benefit from it, organizations must be able to analyze these data efficiently and effectively. This activity is nowadays referred to as business intelligence and one class of algorithms used within this activity are called data mining methods. There are some famous success stories about knowledge discovered via data mining but there is also a lot of disappointment so far. The paper argues that one of the reasons for some failures to produce better results with data mining is the reliance on transactional and master data only. It points to other types of data that can enrich transactional data and help in this way to produce more interesting and more rewarding data patterns.
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paper_title: Customer Base Analysis : Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting

paper_content:

Customer relationship management (CRM) enjoys increasing attention as a countermeasure to switching behaviour of customers. Because foregone profits of (partially) defected customers can be significant, an increase of the retention rate can be very profitable. In this paper we focus on the treatment of a companys most behaviourally loyal customers in a non-contractual setting. We build a model in order to predict partial defection by behaviourally loyal clients using three classification techniques: Logistic regression, automatic relevance determination (ARD) Neural Networks and Random Forests. Focusing on partial attrition of high-frequency shoppers who exhibit a regular visit pattern may overcome the problem of unidentifiability of total defection in non-contractual settings. Classification accuracy (PCC) and area under the receiver operating characteristic curve (AUC) are used to evaluate classifier performance on a test/hold-out sample. Using real-life data from an FMCG retailer, we show that future partial defection can be successfully predicted, i.e. exceeding the benchmark hurdle of the null model. There are no significant differences in terms of performance among alternative classification techniques. Similar to direct-marketing applications we find that past behavioural variables, more specifically RFM variables (recency, frequency, and monetary value) are the best predictors of partial customer defection. This set of variables complements demographic variables confirming findings by other authors about its importance in predicting churn behaviour. Moreover, additional variables (listed in decreasing order of importance) such as the length of customer relationship, mode of payment, buying behaviour across categories, usage of promotions and brand purchase behaviour are shown to be moderately useful to incorporate in attrition models. 2004 Elsevier B.V. All rights reserved.
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paper_title: Business data mining — a machine learning perspective

paper_content:

The objective of this paper is to inform the information systems (IS) manager and business analyst about the role of machine learning techniques in business data mining. Data mining is a fast growing application area in business. Machine learning techniques are used for data analysis and pattern discovery and thus can play a key role in the development of data mining applications. Understanding the strengths and weaknesses of these techniques in the context of business is useful in selecting an appropriate method for a specific application. The paper, therefore, provides an overview of machine learning techniques and discusses their strengths and weaknesses in the context of mining business data. A survey of data mining applications in business is provided to investigate the use of learning techniques. Rule induction (RI) was found to be most popular, followed by neural networks (NNs) and case-based reasoning (CBR). Most applications were found in financial areas, where prediction of the future was a dominant task category.
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paper_title: The impact of preprocessing on data mining: an evaluation of classifier sensitivity in direct marketing

paper_content:

Abstract Corporate data mining faces the challenge of systematic knowledge discovery in large data streams to support managerial decision making. While research in operations research, direct marketing and machine learning focuses on the analysis and design of data mining algorithms, the interaction of data mining with the preceding phase of data preprocessing has not been investigated in detail. This paper investigates the influence of different preprocessing techniques of attribute scaling, sampling, coding of categorical as well as coding of continuous attributes on the classifier performance of decision trees, neural networks and support vector machines. The impact of different preprocessing choices is assessed on a real world dataset from direct marketing using a multifactorial analysis of variance on various performance metrics and method parameterisations. Our case-based analysis provides empirical evidence that data preprocessing has a significant impact on predictive accuracy, with certain schemes proving inferior to competitive approaches. In addition, it is found that (1) selected methods prove almost as sensitive to different data representations as to method parameterisations, indicating the potential for increased performance through effective preprocessing; (2) the impact of preprocessing schemes varies by method, indicating different ‘best practice’ setups to facilitate superior results of a particular method; (3) algorithmic sensitivity towards preprocessing is consequently an important criterion in method evaluation and selection which needs to be considered together with traditional metrics of predictive power and computational efficiency in predictive data mining.
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paper_title: Optimal Selection for Direct Mail

paper_content:

Direct marketing mail is a growing area of marketing practice, yet the academic journals contain very little research on this topic. The most important issue for direct marketers is how to sample targets from a population for a direct mail campaign. Although some selection methods are described in the literature, there seems to be not a single paper discussing the analytical and statistical aspects involved. The objective of this paper is to introduce a comprehensive methodology for the selection of targets from a mailing list for direct mail. At least theoretically, this methodology leads to more efficient selection procedures than the existing ones. The latter are not based on an optimal selection strategy, whereas we explicitly take the profit function into account. By equating marginal costs and marginal returns we determine which households should receive a mailing in order to maximize expected profit. In the empirical part we show that our methodology has great predictive accuracy and generates higher net returns than traditional approaches.
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paper_title: Customer Lifetime Value Models for Decision Support

paper_content:

We present and discuss the important business problem of estimating the effect of marketing activities on the Lifetime Value of a customer in the Telecommunications industry. We discuss the components of this problem, in particular customer value and length of service (or tenure) modeling, and present a novel segment-based approach, motivated by the segment-level view marketing analysts usually employ. We describe in detail how we build on this approach to estimate the effects of retention campaigns on Lifetime Value, and also discuss its application in other situations. Our solution has been successfully implemented by the Business Insight (BI) Professional Services.
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paper_title: Customer Attrition Analysis for Financial Services Using Proportional Hazard Models

paper_content:

This paper studies the topic of customer attrition in the context of a European financial services company. More specifically, we investigate predictors of churn incidence as part of customer relationship management (CRM). We contribute to the existing literature: (1) by combining several different types of predictors into one comprehensive retention model including several ‘new’ types of time-varying covariates related to actual customer behaviour; (2) by analysing churn behaviour based on a truly random sample of the total population using longitudinal data from a data warehouse. Our findings suggest that: (1) demographic characteristics, environmental changes and stimulating ‘interactive and continuous’ relationships with customers are of major concern when considering retention; (2) customer behaviour predictors only have a limited impact on attrition in terms of total products owned as well as the interpurchase time.
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paper_title: The flat maximum effect and linear scoring models for prediction

paper_content:

Our paper challenges the conventional wisdom that the flat maximum inflicts the ‘curse of insensitivity’ on the modelling of judgement and decision processes. In particular, we argue that this widely demonstrated failure on the part of conventional statistical methods to differentiate between competing models has a useful role to play in the development of accessible and economical applied systems, since it allows a low cost choice between systems which vary in their cognitive demands on the user and in their ease of development and implementation. To illustrate our thesis, we take two recent applications of linear scoring models used for credit scoring and for the prediction of sudden infant death. ::: ::: ::: ::: The paper discusses the nature and determinants of the flat maximum as well as its role in applied cognition. Other sections mention certain unanswered questions about the development of linear scoring models and briefly describe competing formulations for prediction.
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paper_title: Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms

paper_content:

One of the key problems in database marketing is the identification and profiling of households that are most likely to be interested in a particular product or service. Principal component analysis (PCA) of customer background information followed by logistic regression analysis of response behavior is commonly used by database marketers. In this paper, we propose a new approach that uses artificial neural networks (ANNs) guided by genetic algorithms (GAs) to target households. We show that the resulting selection rule is more accurate and more parsimonious than the PCA/logit rule when the manager has a clear decision criterion. Under vague decision criteria, the new procedure loses its advantage in interpretability, but is still more accurate than PCA/logit in targeting households.
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paper_title: Quantitative Forecasting — The State of the Art: Extrapolative Models

paper_content:

This paper sponsored by forecasting Study Group of the O.R. Society surveys the alternative extrapolative forecasting models regularly used and examines the various problems that arise. Extrapolative models are particularly appropriate when used for inventory control and materials purchase, and these two situations are considered. The paper concludes with an analysis of the evidence on the forecasting accuracy of extrapolative models.
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paper_title: Generalising about univariate forecasting methods: further empirical evidence

paper_content:

Abstract This paper extends the empirical evidence on the forecasting accuracy of extrapolative methods. The robustness of the major conclusions of the M-Competition data is examined in the context of the telecommunications data of Fildes (1992) . The performance of Robust Trend, found to be a successful method for forecasting the telecommunications data by Fildes, is compared with that of other successful methods using the M-Competition data. Although it is established that the structure of the telecommunications data is more homogeneous than that of the M-Competition data, the major conclusions of the M-Competition continue to hold for this new data set. In addition, while the Robust Trend method is confirmed to be the best performing method for the telecommunications data, for the 1001 M-Competition series, this method is outperformed by methods such as Single or Damped Smoothing. The performance of smoothing methods depended on how the smoothing parameters are estimated. Optimisation at each time origin was more accurate than optimisation at the first time origin, which in turn is shown to be superior to arbitrary (literature based) fixed values. In contrast to the last point, a data based choice of fixed smoothing constants from a cross-sectional study of the time series was found to perform well.
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paper_title: The accuracy of a procedural approach to specifying feedforward neural networks for forecasting

paper_content:

The comparative accuracy of feedforward neural networks (NN) when applied to time series forecasting problems remains uncertain. This is because most studies suffer from either of two defects--they choose the NN from a wide range of alternatives in order to present the forecast accuracy results in the best light or they do not compare the results with suitable benchmarks. In order to overcome both these objections, this paper proposes an objective procedure for specifying a feedforward NN model and evaluates its effectiveness by examining its forecasting performance compared with established benchmarks. After the selection of input nodes based on cross-validation, a three-stage procedure is proposed here which consists of sequentially selecting first the learning rate followed by the number of nodes and the initial weights. This paper shows that NNs only perform robustly if they are built by considering these three factors jointly. In an empirical demonstration of the strength of the approach, those NN models, built by considering all three factors, performed better than other competitive statistical methods when evaluated rigorously on a standard test data set.
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paper_title: Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

paper_content:

In this paper we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.
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paper_title: Neural networks for short-term load forecasting: a review and evaluation

paper_content:

Load forecasting has become one of the major areas of research in electrical engineering, and most traditional forecasting models and artificial intelligence techniques have been tried out in this task. Artificial neural networks (NNs) have lately received much attention, and a great number of papers have reported successful experiments and practical tests with them. Nevertheless, some authors remain skeptical, and believe that the advantages of using NNs in forecasting have not been systematically proved yet. In order to investigate the reasons for such skepticism, this review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting. Our aim is to help to clarify the issue, by critically evaluating the ways in which the NNs proposed in these papers were designed and tested.
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paper_title: Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning

paper_content:

Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a computerized forecasting system to produce initial forecasts and the subsequent judgmental adjustment of these forecasts by the company's demand planners, ostensibly to take into account exceptional circumstances expected over the planning horizon. Making these adjustments can involve considerable management effort and time, but do they improve accuracy, and are some types of adjustment more effective than others? To investigate this, we collected data on more than 60,000 forecasts and outcomes from four supply-chain companies. In three of the companies, on average, judgmental adjustments increased accuracy. However, a detailed analysis revealed that, while the relatively larger adjustments tended to lead to greater average improvements in accuracy, the smaller adjustments often damaged accuracy. In addition, positive adjustments, which involved adjusting the forecast upwards, were much less likely to improve accuracy than negative adjustments. They were also made in the wrong direction more frequently, suggesting a general bias towards optimism. Models were then developed to eradicate such biases. Based on both this statistical analysis and organisational observation, the paper goes on to analyse strategies designed to enhance the effectiveness of judgmental adjustments directly.
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paper_title: Forecasting software: Past, present and future

paper_content:

Abstract We present an overview of the history of forecasting software over the past 25 years, concentrating especially on the interaction between computing and software. Initially we create a framework by describing important developments of computing technology in terms of hardware and software environments. We then concentrate on two different application areas of forecasting software: (1) research oriented forecasting software often used to analyze a small number of series (for example, in market research); and (2) forecasting modules in planning environments which are often partially automated due to the large number of time series involved. Finally we make some suggestions as to where forecasting software has room for improvement.
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paper_title: Providing support for the use of analogies in demand forecasting tasks

paper_content:

Abstract Management judgment is widely used to adjust statistical forecasts in order to take into account special events, such as sales promotions. There is evidence that forecasters often use information from analogous events from the past to help to estimate the effects of an anticipated special event. Unaided forecasters using such an approach may suffer from errors in recall, difficulties in making judgments about similarity, and difficulties in adapting the information from analogous events to match the attributes of the anticipated event. We conducted an experiment to investigate whether a forecasting support system (FSS), which provided users with guidance on similarity judgments and support for adaptation judgments, could lead to more accurate forecasts of the effects of sales promotions. The experiment suggested that a simple, easily implemented form of adaptation support could significantly improve forecast accuracy under some conditions. The support is also likely to be acceptable to potential users.
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paper_title: Principles Of Forecasting A Handbook For Researchers And Practitioners

paper_content:

Thank you very much for downloading principles of forecasting a handbook for researchers and practitioners. As you may know, people have look numerous times for their chosen books like this principles of forecasting a handbook for researchers and practitioners, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their laptop.
---
paper_title: Towards profitability: a utility approach to the credit scoring problem

paper_content:

Since credit scoring was first applied in the 1940s the standard methodology has been to treat consumer lending decisions as binary classification problems, where the goal has been to make the best possible ‘good/bad’ classification of accounts on the basis of their eventual delinquency status. However, the real goal of commercial lending organizations is to forecast continuous financial measures such as contribution to profit, but there has been little research in this area. In this paper, continuous models of customer worth are compared to binary models of customer repayment behaviour. Empirical results show that while models of customer worth do not perform well in terms of classifying accounts by their good/bad status, they significantly outperform standard classification methodologies when ranking accounts based on their financial worth to lenders.
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Title: Forecasting and Operational Research: A Review

Section 1: Introduction
Description 1: Provide an overview of the contributions of operational research (OR) to forecasting and the distinctive opportunities available to OR in forecasting research.

Section 2: Extrapolative Methods
Description 2: Discuss the development and evaluation of extrapolative forecasting methods, including exponential smoothing, ARIMA models, state-space models, and Bayesian approaches.

Section 3: Causal and Multivariate Methods
Description 3: Review the advances in econometric models, focusing on the incorporation of dynamics, and address the difficulties in modeling non-stationary time series.

Section 4: Computer-Intensive Methods
Description 4: Examine the rise and application of computer-intensive methods such as artificial neural networks (ANN), support vector machines (SVM), and data mining techniques in forecasting.

Section 5: Judgemental Forecasting
Description 5: Explore the role of judgement in forecasting, including methods of combining judgement with formal methods and techniques to improve judgemental forecasts.

Section 6: Evaluating Point Forecasts and Estimating Forecast Uncertainty
Description 6: Discuss methods for evaluating forecast accuracy, the use of different error measures, and approaches for estimating forecast uncertainty including the use of prediction intervals.

Section 7: Forecasting for Operations
Description 7: Analyze the use of forecasting methods in operational contexts such as production planning, inventory management, and addressing issues like intermittent demand and the bullwhip effect.

Section 8: Marketing Applications
Description 8: Describe the applications of forecasting models in marketing, focusing on new-product models, market share models, and customer relationship management (CRM).

Section 9: The Role of Computers and Information Systems
Description 9: Investigate the impact of IT and IS developments on forecasting, including the integration of forecasting support systems into broader enterprise resource planning systems.

Section 10: Conclusion: What is OR's Contribution to Forecasting?
Description 10: Summarize OR's contributions to forecasting research and practice, highlight specific areas of success, and discuss future research opportunities and challenges.