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Retail Demand Forecasting: A Comparative Study for Multivariate Time Series

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  • Md Sabbirul Haque
  • Md Shahedul Amin
  • Jonayet Miah

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. "Retail Demand Forecasting: A Comparative Study for Multivariate Time Series," Papers 2308.11939, arXiv.org.
    • Handle: RePEc:arx:papers:2308.11939
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    References listed on IDEAS

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    1. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.
    2. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    3. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2021. "Distributional regression for demand forecasting in e-grocery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 831-842.
    4. Bakas, Dimitrios & Triantafyllou, Athanasios, 2019. "Volatility forecasting in commodity markets using macro uncertainty," Energy Economics, Elsevier, vol. 81(C), pages 79-94.
    5. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
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