Title: Monthly product sales forecast based on hybrid prediction models

Authors: Weidong Lou; Yong Jin; Hailong Lu; Yanghua Gao

Addresses: Information Center, China Tobacco Zhejiang Industrial Co., Ltd., Hangzhou, Zhejiang, 100000, China ' Information Center, China Tobacco Zhejiang Industrial Co., Ltd., Hangzhou, Zhejiang, 100000, China ' Information Center, China Tobacco Zhejiang Industrial Co., Ltd., Hangzhou, Zhejiang, 100000, China ' Information Center, China Tobacco Zhejiang Industrial Co., Ltd., Hangzhou, Zhejiang, 100000, China

Abstract: To address the poor performance of existing methods in predicting monthly product sales and further improve the prediction accuracy, this paper selected three individual prediction models: autoregressive integrated moving average model (ARIMA), linear regression, and support vector regression (SVR). Dynamic weighting factors, determined through grid search, were used to construct a hybrid prediction model. Taking cigarette products as an example, the three models were trained on the monthly sales data of various cigarette grades from 2019 to 2022, and then used to predict the sales data for 2023. A hybrid prediction model was constructed by applying grid search to find the optimal dynamic weighting factors for the three models, fully utilising the advantages of each. Compared to individual prediction models, the hybrid prediction model produced a smaller absolute percentage error, with outputs closer to actual data, making it more in consistent with real-world conditions.

Keywords: monthly product sales; hybrid prediction model; ARIMA model; linear regression model; SVR model.

DOI: 10.1504/IJCSM.2025.147108

International Journal of Computing Science and Mathematics, 2025 Vol.21 No.2, pp.124 - 137

Received: 28 Aug 2024
Accepted: 18 Nov 2024
Published online: 10 Jul 2025 *

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