Research Article

Retail Demand Forecasting: A Comparative Study for Multivariate Time Series

Authors

  • Md Sabbirul Haque Institute of Electrical and Electronics Engineers, Piscataway, NJ, USA
  • Md Shahedul Amin Department of Finance & Economics, University of Tennessee, Chattanooga, TN, USA
  • Jonayet Miah Department of Computer Science, University of South Dakota, South Dakota, USA
  • Duc Minh Cao Department of Economics, University of Tennessee, Knoxville, TN, USA
  • Md Abu Sayed Department of Professional Security Studies, New Jersey City University, New Jersey, USA
  • Sabbir Ahmed Department of Mathematical Science, University of South Dakota, South Dakota, USA

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.

Article information

Journal

Journal of Mathematics and Statistics Studies

Volume (Issue)

4 (4)

Pages

40-46

Published

2023-10-21

How to Cite

Haque, M. S., Amin, M. S., Miah, J., Cao, D. M., Sayed, M. A., & Ahmed, S. (2023). Retail Demand Forecasting: A Comparative Study for Multivariate Time Series. Journal of Mathematics and Statistics Studies, 4(4), 40-46. https://doi.org/10.32996/jmss.2023.4.4.4

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Keywords:

Demand forecasting, machine learning, macroeconomic variable, economic environment, feature importance