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arXiv · 2308.11939

Retail Demand Forecasting: A Comparative Study for Multivariate Time Series

Md Sabbirul Haque·Md Shahedul Amin·Jonayet Miah
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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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Md Sabbirul Haque, Md Shahedul Amin, Jonayet Miah. 2023-08-23. Retail Demand Forecasting: A Comparative Study for Multivariate Time Series. https://arxiv.org/abs/2308.11939

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