Sales Prediction of Walmart Based on Regression Models

Jiayuan Zhang ()
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Jiayuan Zhang: University of Illinois Urbana-Champaign

A chapter in Proceedings of the 2023 International Conference on Finance, Trade and Business Management (FTBM 2023), 2023, pp 411-420 from Springer

Abstract: Abstract In recent years, sales prediction remains a hot and interesting issues in fast sales industry. This study offers a deep dive into Walmart’s sales prediction based on regression models, mainly focused on multiple linear regression models. The paper starts with a brief introduction to Walmart’s history and operations. Subsequently, it shifts the focus to the importance of sales forecasting, prevailing studies, and current research about sales forecasting. Properly predicting future sales is important to a firm’s success, and different methods have their own advantages and limitations. The study also analyzes the dataset, introducing the response and explanatory variables and the regression method used. Then, the paper gives a comprehensive analysis based on five tasks and a multiple linear regression model. After showing the result, the paper provides some insights into the data. Finally, the research offers limitations of the analysis and some future outlooks on sales forecasting. Overall, these results shed light on guiding further exploration of sales prediction.

Keywords: Sales Prediction; Walmart; Linear Model (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-298-9_45

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DOI: 10.2991/978-94-6463-298-9_45

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