Retail Demand Forecasting: A Comparative Study for Multivariate Time Series

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...

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Bibliographic Details
Published in:arXiv.org 2023-08
Main Authors: Haque, Md Sabbirul, Amin, Md Shahedul, Miah, Jonayet
Format: Article
Language:English
Subjects:
Comparative studies
Demand
Forecasting
Global marketing
Machine learning
Mathematical models
Prediction models
Supply chains
Time series
Online Access:Get full text
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Summary: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.
ISSN:2331-8422