Retail Demand Forecasting: A Comparative Study for Multivariate Time
  Series
Md Sabbirul Haque Md Shahedul Amin Jonayet Miah
Accurate demand forecasting in the retail industry is a critical determinant
of financial performance and supply chain efficiency. As global markets become
increasingly interconnected, businesses are turning towards advanced prediction
models to gain a competitive edge. However, existing literature mostly focuses
on historical sales data and ignores the vital influence of macroeconomic
conditions on consumer spending behavior. In this study, we bridge this gap by
enriching time series data of customer demand with macroeconomic variables,
such as the Consumer Price Index (CPI), Index of Consumer Sentiment (ICS), and
unemployment rates. Leveraging this comprehensive dataset, we develop and
compare various regression and machine learning models to predict retail demand
accurately.