Sales Prediction of Walmart Based on Regression Models

DOI

10.2991/978-94-6463-298-9_45How to use a DOI?

Keywords

Sales Prediction; Walmart; Linear Model

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.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiayuan Zhang
PY  - 2023
DA  - 2023/11/30
TI  - Sales Prediction of Walmart Based on Regression Models
BT  - Proceedings of the 2023 International Conference on Finance, Trade and Business Management (FTBM 2023)
PB  - Atlantis Press
SP  - 411
EP  - 420
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-298-9_45
DO  - 10.2991/978-94-6463-298-9_45
ID  - Zhang2023
ER  -