Retail Demand Forecasting: A Comparative Study for Multivariate Time Series

2023/08/23 by Md Sabbirul Haque, Haque, Md Sabbirul, Md Shahedul Amin +3
Business, Management and Accounting · Decision Sciences · #Artificial Intelligence (cs.AI) #Consumer Retail Behavior Studies #FOS: Computer and information sciences #FOS: Economics and business #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Statistical Finance (q-fin.ST)

paper · pdf · doi:10.48550/arxiv.2308.11939

openalex publication_date 2023/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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