Author(s)

Shitian Li ¹, Junzhe Zhang ², Ziyu Zhang ³, Xv Chu ⁴, Lili Song ¹, Xiaojun Wang ¹

Affiliation(s)

¹ Medical Information Engineering, Shandong Traditional Chinese Medicine University, Jinan, China
² Health Management, Shandong Traditional Chinese Medicine University, Jinan, China
³ Ophthalmology and Optometry, Shandong Traditional Chinese Medicine University, Jinan, China
⁴ School of Health, Shandong Traditional Chinese Medicine University, Jinan, China

Corresponding Author

Shitian Li

ABSTRACT

With the rapid development of e-commerce, accurate demand forecasting in the e-commerce retail industry is crucial for inventory optimisation and supply chain management. In this paper, a combined optimisation model based on ARIMA and LSTM is proposed to improve the accuracy and robustness of demand forecasting. Firstly, the data are preprocessed and clustering analysis is performed to group similar categories into one class to simplify the data structure and reduce the modelling complexity; then the ARIMA and LSTM models are used to forecast the demand respectively, and the combined optimization model is constructed by combining the advantages of the two models through difference forecasting. The experimental results show that the model can significantly improve the prediction accuracy, optimise the inventory management, and provide a scientific basis for the resource planning of the e-commerce platform. Finally, this paper analyses the limitations of the model and looks forward to the future research direction.

KEYWORDS

Demand Forecasting, Combinatorial Optimisation Model, ARIMA, LSTM, Inventory Management

CITE THIS PAPER

Shitian Li, Junzhe Zhang, Ziyu Zhang, Xv Chu, Lili Song, Xiaojun Wang, Combinatorial Optimisation Model for E-Commerce Retail Merchant Demand Forecasting Based on ARIMA and LSTM. Information Systems and Economics (2024) Vol. 5: 91-99. DOI: http://dx.doi.org/10.23977/infse.2024.050513.

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