Retail Demand Forecasting: A Comparative Study for Multivariate Time Series
This addresses the need for more accurate demand forecasting in the retail industry to enhance financial performance and supply chain efficiency, but it is incremental as it builds on existing methods by adding new data.
The study tackled the problem of retail demand forecasting by enriching time series data with macroeconomic variables like CPI, ICS, and unemployment rates, and found that machine learning models improved prediction accuracy, though no concrete numbers were provided.
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.