Chapter I Business Forecasting with Artificial Neural Networks : An Overview
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paper_title: Neural networks: applications in industry, business and science

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Just four years ago, the only widely reported commercial application of neural network technology outside the financial industry was the airport baggage explosive detection system [27] developed at Science Applications International Corporation (SAIC). Since that time scores of industrial and commercial applications have come into use, but the details of most of these systems are considered corporate secrets and are shrouded in secrecy. This hastening trend is due in part to the availability of an increasingly wide array of dedicated neural network hardware. This hardware is either in the form of accelerator cards for PCs and workstations or a large number of integrated circuits implementing digital and analog neural networks either currently available or in the final stages of design
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paper_title: Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures

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Motivated by the lack of evidence supporting the conjecture that the back-propagation neural network (BPNN) is a universal approximator thus it can perform at least comparably to linear models on linear data, this study is designed to answer two primary research questions, namely, "how does the BPNN perform with respect to various underlying ARMA(p,q) structures?" and "how does the level of noise in the training time series affect the BPNN's performance?" The goal is to understand better the modelling and forecasting ability of BPNNs on a special class of time series and suggest proper training strategies to improve performance. Using Box-Jenkins models' performance as a benchmark, it is concluded that BPNNs generally performed well and consistently for time series corresponding to ARMA(p,q) structures. BPNNs' ability to model and forecast is not affected by the number of parameters but by the magnitude of the coefficients of the underlying structure. Overall, BPNNs perform significantly better for most of the structures when a particular noise level is considered during network training. Therefore, a proper strategy is to train networks at a noise level consistent in magnitude with the time series' sample standard deviation.
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paper_title: Research Needs in Forecasting

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The demand for research on forecasting is strong. This conclusion is based on the high number of citations to papers published about research on forecasting, and upon the number of subscriptions for journals devoted to forecasting. The supply of research papers is also large, following a rapid growth in the 1960s and 1970s. This research has produced important findings. Despite this, a comparison of published research versus the needs expressed in two surveys of academics and practitioners showed that numerous gaps still exist. A review of the literature also supported this conclusion that the research being produced does not match up well against the research desired. Suggestions are made as to what research is needed and how it should be conducted.
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paper_title: Neural Network Time Series: Forecasting of Financial Markets

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From the Publisher: ::: A neural network is a computer program that can recognise patterns in data, learn from this and (in the case of time series data) make forecasts of future patterns. There are now over 20 commercially available neural network programs designed for use on financial markets and there have been some notable reports of their successful application. However, like any other computer program, neural networks are only as good as the data they are given and the questions that are asked of them. Proper use of a neural network involves spending time understanding and cleaning the data: removing errors, preprocessing and postprocessing. This book takes the reader beyond the 'black-box' approach to neural networks and provides the knowledge that is required for their proper design and use in financial markets forecasting - with an emphasis on futures trading. Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software. For the professional financial forecaster this book is without parallel as a comprehensive, practical and up-to-date guide to this important subject.
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paper_title: Neural Networks For Financial Forecasting

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From the Publisher: ::: When applied to the world of finance, neural networks are automated trading systems, based on mapping inputs and outputs for forecasting probable future values. In Neural Networks for Financial Forecasting - the first book to focus on the role of neural networks specifically in price forecasting - traders are provided with a solid foundation that explains how neural nets work, what they can accomplish, and how to construct, use, and apply them for maximum profit. It is written by an acknowledged authority who is, himself, the developer of several successful networks. Neural Networks for Financial Forecasting enables you to develop a usable, state-of-the-art network from scratch all the way through completion of training. There are spreadsheets and graphs throughout to illustrate key points, and an appendix of valuable information, including neural network software suppliers and related publications.
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paper_title: Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability

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From the Publisher: ::: From mobile communications to robotics to space technology to medical instrumentation, new technologies are demanding increasingly complex methods of digital signal processing (DSP). This book shows researchers how recurrent neural networks can be implemented to expand the range of traditional signal processing techniques. Featuring original research on stability in neural networks, the book combines rigorous mathematical analysis with application examples. Experimental evidence as well as an overview of existing approaches are also included. ::: Market: Engineers working in signal processing, neural networks, communications, nonlinear control, and time series analysis.
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paper_title: Forecasting Exchange Rates Using Feedforward And Recurrent Neural Networks

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In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out-of-sample prediction error relative to the random walk model. Copyright 1995 by John Wiley & Sons, Ltd.
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paper_title: Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities

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Abstract In this paper, two artificial neural networks, a recurrent neural network (RNN) and a three-layer feed-forward back-propagation (BP), are applied for long-term load forecasting. The RNN is designed to forecast the loads of 1 yr ahead and the BP is used to forecast the next 5 and 10 years loads. The proposed networks are trained with the past 20 years (1975–1994) of actual data and are tested for target years 1995–1997, 2000, and 2005. In addition to the target year load forecasting, a sliding window training method is proposed for the continuous retraining of the RNN. The actual data is used for forecasting the loads of 1975–1994. However, forecasted data is applied for forecasting the loads beyond 1994. Since the weather condition data is not available for longer than two weeks ahead, a sensitivity program is developed to produce the future temperature from the present one. Very reasonable results have been obtained for the seen (inner sample) and unseen (out-of-sample or loads of target years) data. In this study, total system load forecast reflecting current and future trends, tempered with good judgement which is the key to all planning and indeed financial success is carried out for nine utilities in Japan.
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paper_title: Recurrent neural networks for short-term load forecasting

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Forecasting the short-term load entails the construction of a model, and, using the information available, estimating the parameters of the model to optimize the prediction performance. It follows that the more closely the chosen model approximates the actual physical generating process, the higher the expected performance of the forecasting system. In this paper it is postulated that the load can be modeled as the output of some dynamic system, influenced by a number of weather, time and other environmental variables. Recurrent neural networks, being members of a class of connectionist models exhibiting inherent dynamic behavior, can thus be used to construct empirical models for this dynamic system. Because of the nonlinear dynamic nature of these models, the behavior of the load prediction system can be captured in a compact and robust representation. This is illustrated by the performance of recurrent models on the short-term forecasting of the nation-wide load for the South African utility, ESKOM. A comparison with feedforward neural networks is also given.
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paper_title: Recurrent neural networks and robust time series prediction

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We propose a robust learning algorithm and apply it to recurrent neural networks. This algorithm is based on filtering outliers from the data and then estimating parameters from the filtered data. The filtering removes outliers from both the target function and the inputs of the neural network. The filtering is soft in that some outliers are neither completely rejected nor accepted. To show the need for robust recurrent networks, we compare the predictive ability of least squares estimated recurrent networks on synthetic data and on the Puget Power Electric Demand time series. These investigations result in a class of recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component. Conventional least squares methods of fitting NARMA(p,q) neural network models are shown to suffer a lack of robustness towards outliers. This sensitivity to outliers is demonstrated on both the synthetic and real data sets. Filtering the Puget Power Electric Demand time series is shown to automatically remove the outliers due to holidays. Neural networks trained on filtered data are then shown to give better predictions than neural networks trained on unfiltered time series. >
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paper_title: Multi-step-ahead prediction using dynamic recurrent neural networks

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Abstract A method for the development of empirical predictive models for complex processes is presented. The models are capable of performing accurate multi-step-ahead (MS) predictions, while maintaining acceptable single-step-ahead (SS) prediction accuracy. Such predictors find applications in model predictive controllers and in fault diagnosis systems. The proposed method makes use of dynamic recurrent neural networks in the form of a nonlinear infinite impulse response (IIR) filter. A learning algorithm is presented, which is based on a dynamic gradient descent approach. The effectiveness of the method for accurate MS prediction is tested on an artificial problem and on a complex, open-loop unstable process. Comparative results are presented with polynomial Nonlinear AutoRegressive with eXogeneous (NARX) predictors, and with recurrent networks trained using teacher forcing. Validation studies indicate that excellent generalization is obtained for the range of operational dynamics studied. The research demonstrates that the proposed network architecture and the associated learning algorithm are quite effective in modeling the dynamics of complex processes and performing accurate MS predictions.
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paper_title: An Analysis of the Applications of Neural Networks in Finance

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Over the last 10 years, neural networks have been increasingly applied to various areas of finance. Neural networks are more often applied on the assets side than on the liabilities side of the balance sheet. Some major characteristics of the areas of these applications are their data intensity, unstructured nature, high degree of uncertainty, and hidden relationships. Most of the applications use the backpropagation model with one hidden layer. In most of these applications, neural networks out-performed traditional statistical models, such as discriminant and regression analysis. Furthermore, these applications have shown significant success in financial practice, for example, in forecasting T-bills, in asset management, in portfolio selection, and in fraud detection.
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paper_title: Neural networks for the prediction and forecasting of water resources variables : a review of modelling issues and applications

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Abstract Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables. In this paper, the steps that should be followed in the development of such models are outlined. These include the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation. The options available to modellers at each of these steps are discussed and the issues that should be considered are highlighted. A review of 43 papers dealing with the use of neural network models for the prediction and forecasting of water resources variables is undertaken in terms of the modelling process adopted. In all but two of the papers reviewed, feedforward networks are used. The vast majority of these networks are trained using the backpropagation algorithm. Issues in relation to the optimal division of the available data, data pre-processing and the choice of appropriate model inputs are seldom considered. In addition, the process of choosing appropriate stopping criteria and optimising network geometry and internal network parameters is generally described poorly or carried out inadequately. All of the above factors can result in non-optimal model performance and an inability to draw meaningful comparisons between different models. Future research efforts should be directed towards the development of guidelines which assist with the development of ANN models and the choice of when ANNs should be used in preference to alternative approaches, the assessment of methods for extracting the knowledge that is contained in the connection weights of trained ANNs and the incorporation of uncertainty into ANN models.
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paper_title: Neural networks for short-term load forecasting: a review and evaluation

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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: Applications of artificial neural networks in management science: a survey

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Abstract An increasing number of publications reporting on applications of Artificial Neural Networks demonstrates the growing relevance of modeling techniques summarized under this heading. In Management Science several known problems also have already been solved by using one of these methods. The paper at hand provides a survey of the most recent publications in this field and employs the functional structuring of Management for presentation. In the spirit of a metaanalysis we try to identify regularities with respect to the methods used and the results achieved. Furthermore we formulate areas for future research and give a summarizing evaluation of the state-of-the-art.
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paper_title: Neural networks in business: a survey of applications (1992–1998)

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Abstract During the last decade, neural networks have established themselves as a theoretically sound alternative to traditional statistical models, and a large body of research on their application to business has been produced. The comprehensive range of business and financial applications is such that a focus is required for an in-depth analysis, therefore this review addresses applications related to management, marketing and decision making. Also, given that previous reviews have dealt with earlier publications, the time span of the review is limited to the period 1992–1998. The presentation is centred on summary tables with links between them. These tables classify the studies according to their application areas, the main contributions rendered by the use of neural networks, and the alleged advantages and disadvantages of this, as well as the journal of publication. Further information on the neural network models, other statistical methods against which they have been compared, and features of the analysed data are also provided. The more controversial issues concerning real-world applications of neural networks are discussed as a part of a critical analysis. Many of the studies are shown to be first attempts to apply these new techniques to established areas of research, whereas only a few tackle real-world cases. Although still regarded as a novel methodology, neural networks are shown to have matured to the point of offering real practical benefits in many of their applications.
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paper_title: A bibliography of neural network business applications research: 1994–1998

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Abstract The purpose of this paper is to present a comprehensive bibliography of neural network application research in business during the period of 1994–1998. Our extensive literature searches have identified a total of 302 research articles. A classification of these articles by year reveals that a large amount of research has been published in the last five years. Production/operations, finance, marketing/distribution, and information systems were found as the most popular application areas. Information on neural network development language/tool, learning paradigm, computing operating environment, journals and authors are included. An in-depth comparison with the previous survey findings and potential future research trend in the neural network business research are discussed. Scope and purpose Due to the breakthrough of the neural network technology, there has been an increasing amount of neural network application research in the last decade. As a result, a considerable amount of published research has appeared, with a significant portion focusing on actual neural network development for business applications. But a comprehensive bibliography of these research published in the last five years is not documented.
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paper_title: Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results

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The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast).
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paper_title: Predicting bankruptcy using recursive partitioning and a realistically proportioned data set

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Auditors must assess their clients' ability to function as a going concern for at least the year following the financial statement date. The audit profession has been severely criticized for failure to ‘blow the whistle’ in numerous highly visible bankruptcies that occurred shortly after unmodified audit opinions were issued. Financial distress indicators examined in this study are one mechanism for making such assessments. This study measures and compares the predictive accuracy of an easily implemented two-variable bankruptcy model originally developed using recursive partitioning on an equally proportioned data set of 202 firms. In this study, we test the predictive accuracy of this model, as well as previously developed logit and neural network models, using a realistically proportioned set of 14,212 firms' financial data covering the period 1981–1990. The previously developed recursive partitioning model had an overall accuracy for all firms ranging from 95 to 97% which outperformed both the logit model at 93 to 94% and the neural network model at 86 to 91%. The recursive partitioning model predicted the bankrupt firms with 33–58% accuracy. A sensitivity analysis of recursive partitioning cutting points indicated that a newly specified model could achieve an all firm and a bankrupt firm predictive accuracy of approximately 85%. Auditors will be interested in the Type I and Type II error tradeoffs revealed in a detailed sensitivity table for this easily implemented model. Copyright © 2000 John Wiley & Sons, Ltd.
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paper_title: Probabilistic Neural Networks in Bankruptcy Prediction

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Abstract Prior application of neural networks in finance and accounting, notably in bankruptcy prediction, are limited to back-propagation neural networks. Their well-known disadvantages, however, limit the practical usefulness of neural discriminant models. Instead, a probabilistic neural network with fewer of these difficulties is proposed. Using data from the U.S. oil and gas industry, alternate methodologies are compared. Whereas probabilistic neural networks without pattern normalization and Fisher discriminant analysis achieve the best overall estimation results, discriminant analysis produces superior results for bankrupt companies.
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paper_title: Option price forecasting using neural networks

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In this research, forecasting of the option prices of Nikkei 225 index futures is carried out using backpropagation neural networks. Different results in terms of accuracy are achieved by grouping the data differently. The results suggest that for volatile markets a neural network option pricing model outperforms the traditional Black-Scholes model. However, the Black-Scholes model is still good for pricing at-the-money options. In using the neural network model, data partition according to moneyness should be applied. Those who prefer less risk and less returns may use the traditional Black-Scholes model results while those who prefer high risk and high return may choose to use the neural network model results.
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paper_title: Predicting US recessions with leading indicators via neural network models

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Abstract This paper examines the relevance of various financial and economic indicators in predicting US recessions via neural network models. We share the view that business cycles are asymmetric and cannot be adequately accommodated by linear constant-parameter single-index models. We employ a novel neural network (NN) to recursively model the relationship between the leading indicators and the probability of a future recession. The out-of-sample results show that via the NN model indicators, such as interest rate spread, Department of Commerce leading index, Stock and Watson index, and S&P500 index are useful in predicting US recessions, including the most recent one in the early 1990s. Furthermore, when the out-of-sample forecasting period is divided into three subperiods, we find that the relevance of various leading indicators may change from time to time.
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paper_title: A comparative study of medium-weather-dependent load forecasting using enhanced artificial/fuzzy neural network and statistical techniques

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Abstract Monthly peak load demand of Jeddah area for the past nine years is used for investigation throughout this work. The first seven years data is used for training while the prediction is carried out for the following two years. First, Minitab statistical software package is used for peak load prediction using autoregressive integrated moving average (ARIMA) technique, and an average error value of 11.7% is achieved. Next, an artificial neural network (ANN) is utilised and several suggestions are implemented to build an adaptive form of ANN. Direct ANN implementation shows poor performance. Also, fuzzy neural network (FNN) is also examined but showed comparatively poor performance. The modelling of the trend of peak load demand is incorporated by introducing “time index feature” and that clearly enhanced the performance of both ANN (6.8% error) and FNN (4.7% error). A comparative study is provided below to show the accuracy of distinction among these techniques.
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paper_title: Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index

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In this study, we attempt to model and predict the direction of return on market index of the Taiwan Stock Exchange, one of the fastest growing financial exchanges in developing Asian countries. Our motivation is based on the notion that trading strategies guided by forecasts of the direction of price movement may be more effective and lead to higher profits. The probabilistic neural network (PNN) is used to forecast the direction of index return after it is trained by historical data. Statistical performance of the PNN forecasts are measured and compared with that of the generalized methods of moments (GMM) with Kalman filter. Moreover, the forecasts are applied to various index trading strategies, of which the performances are compared with those generated by the buy-and-hold strategy as well as the investment strategies guided by forecasts estimated by the random walk model and the parametric GMM models. Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. Influences of length of investment horizon and commission rate are also considered.
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paper_title: A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice

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This paper presents a definitive description of neural network methodology and provides an evaluation of its advantages and disadvantages relative to statistical procedures. The development of this rich class of models was inspired by the neural architecture of the human brain. These models mathematically emulate the neurophysical structure and decision making of the human brain, and, from a statistical perspective, are closely related to generalized linear models. Artificial neural networks are, however, nonlinear and use a different estimation procedure feed forward and back propagation than is used in traditional statistical models least squares or maximum likelihood. Additionally, neural network models do not require the same restrictive assumptions about the relationship between the independent variables and dependent variables. Consequently, these models have already been very successfully applied in many diverse disciplines, including biology, psychology, statistics, mathematics, business, insurance, and computer science. ::: ::: We propose that neural networks will prove to be a valuable tool for marketers concerned with predicting consumer choice. We will demonstrate that neural networks provide superior predictions regarding consumer decision processes. In the context of modeling consumer judgment and decision making, for example, neural network models can offer significant improvement over traditional statistical methods because of their ability to capture nonlinear relationships associated with the use of noncompensatory decision rules. Our analysis reveals that neural networks have great potential for improving model predictions in nonlinear decision contexts without sacrificing performance in linear decision contexts. ::: ::: This paper provides a detailed introduction to neural networks that is understandable to both the academic researcher and the practitioner. This exposition is intended to provide both the intuition and the rigorous mathematical models needed for successful applications. In particular, a step-by-step outline of how to use the models is provided along with a discussion of the strengths and weaknesses of the model. We also address the robustness of the neural network models and discuss how far wrong you might go using neural network models versus traditional statistical methods. ::: ::: Herein we report the results of two studies. The first is a numerical simulation comparing the ability of neural networks with discriminant analysis and logistic regression at predicting choices made by decision rules that vary in complexity. This includes simulations involving two noncompensatory decision rules and one compensatory decision rule that involves attribute thresholds. In particular, we test a variant of the satisficing rule used by Johnson et al. Johnson, Eric J., Robert J. Meyer, Sanjoy Ghose. 1989. When choice models fail: Compensatory models in negatively correlated environments. J. Marketing Res.26August 255--270. that sets a lower bound threshold on all attribute values and a “latitude of acceptance” model that sets both a lower threshold and an upper threshold on attribute values, mimicking an “ideal point” model Coombs and Avrunin [Coombs, Clyde H., George S. Avrunin. 1977. Single peaked functions and the theory of preference. Psych. Rev.84 216--230.]. We also test a compensatory rule that equally weights attributes and judges the acceptability of an alternative based on the sum of its attribute values. Thus, the simulations include both a linear environment, in which traditional statistical models might be deemed appropriate, as well as a nonlinear environment where statistical models might not be appropriate. The complexity of the decision rules was varied to test for any potential degradation in model performance. For these simulated data it is shown that, in general, the neural network model outperforms the commonly used statistical procedures in terms of explained variance and out-of-sample predictive accuracy. ::: ::: An empirical study bridging the behavioral and statistical lines of research was also conducted. Here we examine the predictive relationship between retail store image variables and consumer patronage behavior. A direct comparison between a neural network model and the more commonly encountered techniques of discriminant analysis and factor analysis followed by logistic regression is presented. Again the results reveal that the neural network model outperformed the statistical procedures in terms of explained variance and out-of-sample predictive accuracy. We conclude that neural network models offer superior predictive capabilities over traditional statistical methods in predicting consumer choice in nonlinear and linear settings.
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paper_title: Using neural networks to forecast the systematic risk of stocks

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Abstract This paper discusses different methods of predicting a stock's systematic risk, using the financial statements of 67 German corporations from the period 1967 to 1986. We show that the most precise forecasts are given by neural networks, whose topology has been optimized by a genetic algorithm. In addition we analyze and visualize the dependencies that influence the forecasts of a stock's systematic risk.
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paper_title: A comparison of artificial neural network and time series models for forecasting commodity prices

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Abstract A feedforward neural network which can account for nonlinear relationships was used to compare ARIMA and neural network price forecasting performance. Data used was monthly live cattle and wheat prices from 1950 through 1990. The experiment was repeated seven times for successive three year periods. This involved using a walk forward or sliding window approach from 1970 through 1990 which generated out of sample results. The neural network models achieved a 27 percent and 56 percent lower mean squared error than ARIMA model. The absolute mean error and mean absolute percent error were also lower for the neural network models. The neural network models were able to capture a significant number of turning points for both wheat and cattle, while the ARIMA model was only able to do so for wheat. Since this forecasting method is not problem specific and uses only past prices, it can be applied to other forecasting problems such as stocks and other financial prices.
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paper_title: Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks

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This is an extension of prior studies that have used artificial neural networks to predict bankruptcy. The incremental contribution of this study is threefold. First, we use only financially stressed firms in our control sample. This enables the models to more closely approximate the actual decision processes of auditors and other interested parties. Second, we develop a more parsimonious model using qualitative ‘bad news’ variables that prior research indicates measure financial distress. Past research has focused on the ‘usefulness’ of accounting numbers and therefore often ignored non-accounting variables that may contribute to the classification accuracy of the distress prediction models. In addition, rather than use multiple financial ratios, we include a single variable of financial distress using the Zmijewski distress score that incorporates ratios measuring profitability, liquidity, and solvency. Finally, we develop and test a genetic algorithm neural network model. We examine its predictive ability to that of a backpropagation neural network and a model using multiple discriminant analysis. The results indicate that the misclassification cost of the genetic algorithm-based neural network was the lowest among the models. Copyright © 2001 John Wiley & Sons, Ltd.
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paper_title: Forecasting consumers' expenditure: A comparison between econometric and neural network models

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Abstract This paper is motivated by the difficulties faced by forecasters in predicting the decline in the growth rate of consumers' expenditure in the late 1980s. The econometric specifications of four competing explanations are replicated and the static forecasts compared with the actual outturns. The same data are then used to estimate neural network models. The main issue is whether the neural network technology can extract any more from the data sets provided than the econometric approach. It is found that the neural network models describe the decline in the growth of consumption since the late 1980s as well as, but no better than, the econometric specifications included in the exercise, and are shown to be robust when faced with a small number of data points. However, whichever approach is adopted, it is the skill of choosing the menu of explanatory variables which determines the success of the final results.
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paper_title: Nonlinear Predictability of Stock Returns Using Financial and Economic Variables

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Inspired by the linear predictability and nonlinearity found in the finance literature, this article examines the nonlinear predictability of the excess returns. The relationship between the excess returns and the predicting variables is recursively modeled by a neural-network model, which is capable of performing flexible nonlinear functional approximation. The nonlinear neural-network model is found to have better in-sample fit and out-of-sample forecasts compared to its linear counterpart. Moreover, the switching portfolio based on the recursive neural-network forecasts generates higher profits with lower risks than both the buy-and-hold market portfolio and the switching portfolio based on linear recursive forecasts.
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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: An object-oriented neural network approach to short-term traffic forecasting

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Abstract This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90–94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93–95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction.
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paper_title: A comparison of nonlinear methods for predicting earnings surprises and returns

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We compare four nonlinear methods on their ability to learn models from data. The problem requires predicting whether a company will deliver an earnings surprise a specific number of days prior to announcement. This problem has been well studied in the literature using linear models. A basic question is whether machine learning-based nonlinear models such as tree induction algorithms, neural networks, naive Bayesian learning, and genetic algorithms perform better in terms of predictive accuracy and in uncovering interesting relationships among problem variables. Equally importantly, if these alternative approaches perform better, why? And how do they stack up relative to each other? The answers to these questions are significant for predictive modeling in the financial arena, and in general for problem domains characterized by significant nonlinearities. In this paper, we compare the four above-mentioned nonlinear methods along a number of criteria. The genetic algorithm turns out to have some advantages in finding multiple "small disjunct" patterns that can be accurate and collectively capable of making predictions more often than its competitors. We use some of the nonlinearities we discovered about the problem domain to explain these results.
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paper_title: Short-term inter-urban traffic forecasts using neural networks

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Abstract Back-propagation neural networks were trained to make short-term forecasts of traffic flow, speed and occupancy in the Utrecht/Rotterdam/Hague region of The Netherlands. A problem which had to be faced when designing the system was the vast number of possible input parameters. Whilst neural networks which utilised all available inputs performed well, their size made them impractical for implementation. A technique of stepwise reduction of network size was developed by elasticity testing the large neural networks, showing a way of overcoming this difficulty. Results for occupancy and flow forecasts by this method show some promise, but do not out-perform naive predictors. Forecasts of vehicle speed were much less successful, perhaps because of the distorting effect of slow moving vehicles, particularly in low flow conditions. The elasticity tests were found to be useful, not only as a means of enabling network size reduction, but as a means of interpreting the neural network model.
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paper_title: Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model

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Abstract We empirically compare the forecasting ability of artificial neural network (ANN) with multinomial logit model (MNL) in the context of frequently purchased grocery products for a retailer. Using scanner data on three grocery product categories, we find that performance of ANN compares favorably to MNL in forecasting brand shares. We test the sensitivity of the forecasting error in the two approaches to the length of the estimation period and the clustering of households which is used to define homogenous segments of households. We find the results to be robust to these variations. We also derive a few empirical propositions regarding performance of ANN and MNL from our analysis. The results are consistent with those in Kumar, Rao and Soni (1995) and suggest that although neural networks suffer from interpretability problem, they are a useful method to forecast brand shares in grocery product categories where large amounts of scanner data are readily available.
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paper_title: Forecasting Daily Foreign Exchange Rates Using Genetically Optimized Neural Networks

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Forecasting currency exchange rates is an important financial problem that has received much attention especially because of its intrinsic difficulty and practical applications. The statistical distribution of foreign exchange rates and their linear unpredictability are recurrent themes in the literature of international finance. Failure of various structural econometric models and models based on linear time series techniques to deliver superior forecasts to the simplest of all models, the simple random walk model, have prompted researchers to use various non-linear techniques. A number of non-linear time series models have been proposed in the recent past for obtaining accurate prediction results, in an attempt to ameliorate the performance of simple random walk models. In this paper, we use a hybrid artificial intelligence method, based on neural network and genetic algorithm for modelling daily foreign exchange rates. A detailed comparison of the proposed method with non-linear statistical models is also performed. The results indicate superior performance of the proposed method as compared to the traditional non-linear time series techniques and also fixed-geometry neural network models. Copyright © 2002 by John Wiley & Sons, Ltd.
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paper_title: Stock market trend prediction using ARIMA-based neural networks

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We develop a prediction system useful in forecasting mid-term price trend in Taiwan stock market (Taiwan stock exchange weighted stock index, abbreviated as TSEWSI). The system is based on a recurrent neural network trained by using features extracted from ARIMA analyses. By differencing the raw data of the TSEWSI series and then examining the autocorrelation and partial autocorrelation function plots, the series can be identified as a nonlinear version of ARIMA(1,2,1). Neural networks trained by using second difference data are shown to give better predictions than otherwise trained by using raw data. During backpropagation training, in addition to the traditional error modification term, we also feedback the difference of two successive predictions in order to adjust the connection weights. Empirical results shows that the networks trained using 4-year weekly data is capable of predicting up to 6 weeks market trend with acceptable accuracy.
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paper_title: Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks

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This paper compares the predictive performance of artificial neural networks (ANN) and multiple regression analysis (MRA) for single family housing sales. Multiple comparisons are made between the two data models in which the data sample size is varied, the funcional specifications is varied, and the temporal prediction is varied. We conclude that ANN performs better than MRA when a moderate to large data sample size is used. For our application, this "moderate to large data sample size" varied from 13% to 39% of the total data sample (506 to 1506 observations out of 3906 total observations). Our results give a plausible explanation why previous papers have obtained varied results when comparing MRA and ANN predictive performance for housing values.
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paper_title: Intelligent simulation and prediction of traffic flow dispersion

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Dispersion of traffic flow on urban road segments is often described by some typical statistical models such as the normal distribution model and the geometric distribution model. These probability-based models can fit traffic flow well under ideal physical environments but may not work satisfactory in certain complex cases because of their strict mathematical assumptions. A neural network-based system identification approach is used to establish an auto-adaptive model for simulating traffic flow dispersion. This model, being feasible to a wide variety of traffic circumstances, can be calibrated and used for on-line traffic flow forecasting. Data simulation and field-testing show reliable performance of the proposed intelligent approach.
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paper_title: A comparison of linear regression and neural network methods for predicting excess returns on large stocks

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Recent studies have shown that there is predictable variation in returns of financial assets over time. We investigate whether the predictive power of the economic and financial variables employed in the above studies can be enhanced if the statistical method of linear regression is replaced by feedforward neural networks with backpropagation of error. A shortcoming of backpropagation networks is that too many free parameters allow the neural network to fit the training data arbitrarily closely resulting in an "overfitted" network. Overfitted networks have poor generalization capabilities. We explore two methods that attempt to overcome this shortcoming by reducing the complexity of the network. The results of our experiments confirm that an "overfitted" network, while making better predictions for within-sample data, makes poor predictions for out-of-sample data. The methods for reducing the complexity of the network, explored in this paper, clearly help improve out-of-sample forecasts. We show that one cannot say that the linear regression forecasts are conditionally efficient with respect to the neural networks forecasts with any degree of confidence. However, one can say that the neural networks forecasts are conditionally efficient with respect to the linear regression forecasts with some confidence. Copyright Kluwer Academic Publishers 1998
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paper_title: Neural networks in business: a survey of applications (1992–1998)

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Abstract During the last decade, neural networks have established themselves as a theoretically sound alternative to traditional statistical models, and a large body of research on their application to business has been produced. The comprehensive range of business and financial applications is such that a focus is required for an in-depth analysis, therefore this review addresses applications related to management, marketing and decision making. Also, given that previous reviews have dealt with earlier publications, the time span of the review is limited to the period 1992–1998. The presentation is centred on summary tables with links between them. These tables classify the studies according to their application areas, the main contributions rendered by the use of neural networks, and the alleged advantages and disadvantages of this, as well as the journal of publication. Further information on the neural network models, other statistical methods against which they have been compared, and features of the analysed data are also provided. The more controversial issues concerning real-world applications of neural networks are discussed as a part of a critical analysis. Many of the studies are shown to be first attempts to apply these new techniques to established areas of research, whereas only a few tackle real-world cases. Although still regarded as a novel methodology, neural networks are shown to have matured to the point of offering real practical benefits in many of their applications.
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paper_title: SHOULD WE USE NEURAL NETWORKS OR STATISTICAL MODELS FOR SHORT-TERM MOTORWAY TRAFFIC FORECASTING?

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This article discusses the relative merits of neural networks and time series methods for traffic forecasting and summarises the findings from a comparative study of their performance for motorway traffic in France. Whilst it was possible to get a good performance with both neural networks and traditional Auto-Regressive Integrated Moving Average (ARIMA) models when forecasting up to an hour ahead using data supplied in 30-min intervals, a purpose-built pattern based forecasting model known as ATHENA, developed by INRETS, out-performed both these methods somewhat. The ways in which these models relate to the structure of traffic data are discussed and alternative paradigms are proposed.
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paper_title: Residential construction demand forecasting using economic indicators: a comparative study of artificial neural networks and multiple regression

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In recent years, demand for residential construction has been growing rapidly in Singapore. This paper proposes the use of economic indicators to predict demand for residential construction in Singapore. At the same time, two forecasting techniques are applied, namely, Artificial Neural Networks (ANN) and Multiple Regression (MR), the former being a state-of-the-art technique while the latter a conventional one. A comparative study is carried out to determine whether the use of economic indicators with the application of the ANN technique can produce better predictions than with the MR method. A total of 12 economic indicators are identified as significantly related to demand for residential construction. Quarterly data from these 12 indicators are used to develop the ANN model. In order to assess the forecasting performance of this state-of-the-art technique, the same set of data is used to develop a conventional MR model. A comparison is made between the two models, in terms of their forecasting accurac...
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paper_title: Forecasting the 30-year U.S. Treasury Bond with a System of Neural Networks

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A forecasting model based on a system of artificial neural networks (ANN) is used to predict the direction of the 30-Year U.S. Treasury Bond on a weekly basis. At the close of Friday's market, the 23-variable database is updated with the latest information and the data pre-processed for input to the prediction system. Thirty-two feed-forward neural networks are trained on the new data and then individually recalled to predict the following Friday's market direction. These results are then used as input to a decision model that ultimately determines the final prediction. Forecasting began in 1989, with live trades commencing in 1990. The average accuracy of the buy prediction was 67% over a five-year period, with an average annualized return on investment (ROI) of 17.3%. This compares with an ROI of 13.9% for the Lehman Brothers T-Bond Index. This paper describes the methods used for data selection, training and testing, the basic system architecture, and how the decision model improved the total system accuracy as compared to individual networks.
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paper_title: Marketing Category Forecasting: An Alternative of BVAR‐Artificial Neural Networks*

paper_content:

Analyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models.
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paper_title: FORECASTING MARKET TRENDS WITH NEURAL NETWORKS

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Abstract Neural networks are just one of the many technologies that are giving businesses a competitive edge. This article explains how neural networks work and how they can be applied in the real estate and financial industries.
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paper_title: Neural network forecasting of quarterly accounting earnings

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Abstract This study uses an artificial neural network model to forecast quarterly accounting earnings for a sample of 296 corporations trading on the New York stock exchange. The resulting forecast errors are shown to be significantly larger (smaller) than those generated by the parsimonious Brown-Rozeff and Griffin-Watts (Foster) linear time series models, bringing into question the potential usefulness of neural network models in forecasting quarterly accounting earnings. This study confirms the conjecture by Chatfield and Hill et al. that neural network models are context sensitive. In particular, this study shows that neural network models are not necessarily superior to linear time series models even when the data are financial, seasonal and non-linear.
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paper_title: Forecasting the Short-term Demand for Electricity: Do Neural Networks Stand a Better Chance?

paper_content:

Keywords: LTS1 Reference LTS-ARTICLE-2000-009 Record created on 2006-06-14, modified on 2016-08-08
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paper_title: An Adaptive Approach to Market Development Forecasting

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In the field of time series models for forecasting, the commonly accepted fact is that no one model could be shown to be superior to all others. An effective time series model for forecasting must incorporate the specific characteristics of the targeted problem domain. This paper proposes a neura network model for market development forecasting In this model, monotonicity and knowledge of seasonal period are incorporated into neural network training. The model is superior to the traditional curve fitting methods, in that it is adaptive in modelling trend and season factors for the time series in cases where the growth curve functions and seasonal functions are a priori unknown. The model is superior to unconstrained neural networks for time series modelling in that random fluctuations can be avoided. An example of forecasting daily sales using the neural network model is demonstrated.
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paper_title: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks

paper_content:

Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing price. Our results indicate that all the networks are feasible, the primary preference being one of convenience.
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paper_title: Commodity futures trading performance using neural network models versus ARIMA models

paper_content:

Neural networks trading returns are compared out‐of‐sample with traditional ARIMA returns for corn, silver, and deutsche mark. Results show that neural network and ARIMA models had positive returns, and at about the same levels. However, deutsche mark was less profitable and returns were not statistically different from zero, in contrast to corn and silver. © 1998 John Wiley & Sons, Inc. Jrl Fut Mark 18: 965–983, 1998
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paper_title: Time-Series Forecasting

paper_content:

As we go through life, everyone makes forecasts all the time, often without realising it. Sadly these forecasts are often (very) inaccurate. Chris Chatfield looks at the chequered history of forecasting and asks how we might do it better using time-series data, and what statistical techniques and models might help us.
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paper_title: The Cascade-Correlation Learning Architecture

paper_content:

Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology. Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network.
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paper_title: An investigation of model selection criteria for neural network time series forecasting

paper_content:

Abstract Artificial neural networks (ANNs) have received more and more attention in time series forecasting in recent years. One major disadvantage of neural networks is that there is no formal systematic model building approach. In this paper, we expose problems of the commonly used information-based in-sample model selection criteria in selecting neural networks for financial time series forecasting. Specifically, Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) as well as several extensions have been examined through three real time series of Standard and Poor’s 500 index (S&P 500 index), exchange rate, and interest rate. In addition, the relationship between in-sample model fitting and out-of-sample forecasting performance with commonly used performance measures is also studied. Results indicate that the in-sample model selection criteria we investigated are not able to provide a reliable guide to out-of-sample performance and there is no apparent connection between in-sample model fit and out-of-sample forecasting performance.
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paper_title: Designing a Neural Network for Forecasting Financial and Economic Time Series

paper_content:

Abstract Artificial neural networks are universal and highly flexible function approximators first used in the fields of cognitive science and engineering. In recent years, neural network applications in finance for such tasks as pattern recognition, classification, and time series forecasting have dramatically increased. However, the large number of parameters that must be selected to develop a neural network forecasting model have meant that the design process still involves much trial and error. The objective of this paper is to provide a practical introductory guide in the design of a neural network for forecasting economic time series data. An eight-step procedure to design a neural network forecasting model is explained including a discussion of tradeoffs in parameter selection, some common pitfalls, and points of disagreement among practitioners.
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paper_title: ANNSTLF-a neural-network-based electric load forecasting system

paper_content:

A key component of the daily operation and planning activities of an electric utility is short-term load forecasting, i.e., the prediction of hourly loads (demand) for the next hour to several days out. The accuracy of such forecasts has significant economic impact for the utility. This paper describes a load forecasting system known as ANNSTLF (artificial neural-network short-term load forecaster) which has received wide acceptance by the electric utility industry and presently is being used by 32 utilities across the USA and Canada. ANNSTLF can consider the effect of temperature and relative humidity on the load. Besides its load forecasting engine, ANNSTLF contains forecasters that can generate the hourly temperature and relative humidity forecasts needed by the system. ANNSTLF is based on a multiple ANN strategy that captures various trends in the data. Both the first and the second generation of the load forecasting engine are discussed and compared. The building block of the forecasters is a multilayer perceptron trained with the error backpropagation learning rule. An adaptive scheme is employed to adjust the ANN weights during online forecasting. The forecasting models are site independent and only the number of hidden layer nodes of ANN's need to be adjusted for a new database. The results of testing the system on data from ten different utilities are reported.
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paper_title: Time series forecasting using neural networks: should the data be deseasonalized first?

paper_content:

This research investigates whether prior statistical deseasonalization of data is necessary to produce more accurate neural network forecasts. Neural networks trained with deseasonalized data from Hill et al. (1996) were compared with neural networks estimated without prior deseasonalization. Both sets of neural networks produced forecasts for the 68 monthly time series from the M-competition (Makridakis et al., 1982). Results indicate that when there was seasonality in the time series, forecasts from neural networks estimated on deseasonalized data were significantly more accurate than the forecasts produced by neural networks that were estimated using data which were not deseasonalized. The mixed results from past studies may be due to inconsistent handling of seasonality. Our findings give guidance to both practitioners and researchers developing neural networks. Copyright © 1999 John Wiley & Sons, Ltd.
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paper_title: Artificial neural networks in accounting and finance: modeling issues

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This article reviews the literature on artificial neural networks (ANNs) applied to accounting and finance problems and summarizes the ‘suggestions’ from this literature. The first section reviews the basic foundation of ANNs to provide a common basis for further elaboration and suggests criteria that should be used to determine whether the use of an ANN is appropriate. The second section of the paper discusses development of ANN models including: selection of the learning algorithm, choice of the error and transfer functions, specification of the architecture, preparation of the data to match the architecture, and training of the network The final section presents some general guidelines and a brief summary of research progress and open research questions. Copyright © 2000 John Wiley & Sons, Ltd.
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paper_title: Pruning algorithms-a survey

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A rule of thumb for obtaining good generalization in systems trained by examples is that one should use the smallest system that will fit the data. Unfortunately, it usually is not obvious what size is best; a system that is too small will not be able to learn the data while one that is just big enough may learn very slowly and be very sensitive to initial conditions and learning parameters. This paper is a survey of neural network pruning algorithms. The approach taken by the methods described here is to train a network that is larger than necessary and then remove the parts that are not needed. >
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paper_title: Neural Network Time Series: Forecasting of Financial Markets

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From the Publisher: ::: A neural network is a computer program that can recognise patterns in data, learn from this and (in the case of time series data) make forecasts of future patterns. There are now over 20 commercially available neural network programs designed for use on financial markets and there have been some notable reports of their successful application. However, like any other computer program, neural networks are only as good as the data they are given and the questions that are asked of them. Proper use of a neural network involves spending time understanding and cleaning the data: removing errors, preprocessing and postprocessing. This book takes the reader beyond the 'black-box' approach to neural networks and provides the knowledge that is required for their proper design and use in financial markets forecasting - with an emphasis on futures trading. Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software. For the professional financial forecaster this book is without parallel as a comprehensive, practical and up-to-date guide to this important subject.
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paper_title: Strategies for Modelling Nonlinear Time-Series Relationships

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Building models of nonlinear relationships are inherently more difficult than linear ones. There are more possibilities, many more parameters, and, thus, more mistakes can be made. It is suggested that a strategy be applied when attempting such modeling involving testing for linearity, considering just a few model types of parsimonious form and then performing postsample evaluation of the resulting models compared to a linear one. The strategy proposed is a 'simple-to-general'one and the application of a heteroskedasticity correction is not recommended. Copyright 1993 by The Economic Society of Australia.
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paper_title: A Model Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks

paper_content:

We take a model-selection approach to the question of whether forward-interest rates are useful in predicting future spot rates, using a variety of out-of-sample forecast-based model-selection criteria—forecast mean squared error, forecast direction accuracy, and forecast-based trading-system profitability. We also examine the usefulness of a class of novel prediction models called artificial neural networks and investigate the issue of appropriate window sizes for rolling-window-based prediction methods. Results indicate that the premium of the forward rate over the spot rate helps to predict the sign of future changes in the interest rate. Furthermore, model selection based on an in-sample Schwarz information criterion (SIC) does not appear to be a reliable guide to out-of-sample performance in the case of short-term interest rates. Thus, the in-sample SIC apparently fails to offer a convenient shortcut to true out-of-sample performance measures.
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paper_title: A simulation study of artificial neural networks for nonlinear time-series forecasting

paper_content:

Abstract This study presents an experimental evaluation of neural networks for nonlinear time-series forecasting. The effects of three main factors — input nodes, hidden nodes and sample size, are examined through a simulated computer experiment. Results show that neural networks are valuable tools for modeling and forecasting nonlinear time series while traditional linear methods are not as competent for this task. The number of input nodes is much more important than the number of hidden nodes in neural network model building for forecasting. Moreover, large sample is helpful to ease the overfitting problem. Scope and purpose Interest in using artificial neural networks for forecasting has led to a tremendous surge in research activities in the past decade. Yet, mixed results are often reported in the literature and the effect of key modeling factors on performance has not been thoroughly examined. The lack of systematic approaches to neural network model building is probably the primary cause of inconsistencies in reported findings. In this paper, we present a systematic investigation of the application of neural networks for nonlinear time-series analysis and forecasting. The purpose is to have a detailed examination of the effects of certain important neural network modeling factors on nonlinear time-series modeling and forecasting.
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paper_title: Neural Networks and the Bias/Variance Dilemma

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Feedforward neural networks trained by error backpropagation are examples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments involving artificial data as well as handwritten numerals. In way of conclusion, we suggest that current-generation feedforward neural networks are largely inadequate for difficult problems in machine perception and machine learning, regardless of parallel-versus-serial hardware or other implementation issues. Furthermore, we suggest that the fundamental challenges in neural modeling are about representation rather than learning per se. This last point is supported by additional experiments with handwritten numerals.
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paper_title: Research prospective on neural network forecasting

paper_content:

A hydraulic coupler for coupling together two hydraulic line portions and which permits quick disconnection of the line portions, has valve means for turning off the flow and preventing loss of fluid from the lines when disconnected, and is made so that connections can be made even though there is pressure present in both of the hydraulic line portions, neither of the hydraulic line portions, or only one of the line hydraulic portions. The coupler is made from a unitary body and other parts that can be manufactured on automatic screw machines and punch presses to insure that the cost of production remains low.
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paper_title: A neural network short term load forecasting model for the Greek power system

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This paper presents the development of an artificial neural network (ANN) based short-term load forecasting model for the Energy Control Center of the Greek Public Power Corporation (PPC). The model can forecast daily load profiles with a lead time of one to seven days. Attention was paid for the accurate modeling of holidays. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training data set are described in the paper. The results indicate that the load forecasting model developed provides accurate forecasts.
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paper_title: Statistically significant forecasting improvements : how much q out-of-sample data is likely necessary ? *

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Abstract Testing the out-of-sample forecasting superiority of one model over another requires an a priori partitioning of the data into a model specification/estimation (‘training’) period and a model comparison/evaluation (‘out-of-sample’ or ‘validation’) period. How large a validation period is necessary for a given mean square forecasting error (MSFE) improvement to be statistically significant at the 5% level? If the forecast errors from each model are NIID and these errors are independent of one another, then the 5% critical points for the F distribution provide the answer to this question. But even optimal forecast errors from well-specified models can be serially correlated. And forecast errors are typically substantially crosscorrelated. For such errors, a validation period in excess of 100 observations long is typically necessary in order for a 20% MSFE reduction to be statistically significant at the 5% level. Illustrative applications using actual economic data are given.
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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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Title: Chapter I Business Forecasting with Artificial Neural Networks: An Overview

Section 1: INTRODUCTION
Description 1: Introduce the significance, history, and relevance of business forecasting with artificial neural networks (ANNs).

Section 2: ARTIFICIAL NEURAL NETWORKS
Description 2: Describe the fundamental concepts, structure, and types of artificial neural networks used in forecasting.

Section 3: FORECASTING APPLICATIONS OF NEURAL NETWORKS
Description 3: Illustrate various business forecasting applications where artificial neural networks have been successfully implemented.

Section 4: ISSUES IN ANN MODELING AND FORECASTING
Description 4: Discuss the critical issues and considerations in developing, training, and validating ANN models for forecasting purposes.

Section 5: CONCLUSION
Description 5: Summarize the key points discussed in the chapter and emphasize the importance and limitations of using ANNs for business forecasting.