Forecasting Retail Sales Via the Use of Stacking Model

Che Sun ()
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Che Sun: Shanghai University, Sino-European School of Technology of Shanghai

A chapter in Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), 2022, pp 405-411 from Springer

Abstract: Abstract Nowadays, the march of machine learning brings about the improvements of companies’ ability to respond the changes in the marketplace and enables them to balance more easily the supply and demand. Thus, predicting based on historical data is getting more and more prevalent. There are numerous approaches applied to attain better results in this research area. The data in this research is from Kaggle and is genuine data provided by 1C company. This paper adopts six models, i.e., Linear Regression, Ridge regression, Random Forest, GBDT, XGBOOST and Stacking to forecast the future sales of retail products based on the historical data. The root mean square error between the real and anticipated data is utilized as performance evaluation. And the results show that the stacking method presents the best performance.

Keywords: machine learning; predict; models; stacking (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-036-7_59

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DOI: 10.2991/978-94-6463-036-7_59

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