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Research Article

Research on Fresh Produce Sales Prediction Algorithm for Store Based on Multidimensional Time Series Data Analysis

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  • @ARTICLE{10.4108/eetsis.3844,
        author={Zhiyu Li and Wei Yu and Wenwei Zhu and Haojie Wan and Jingjing Peng and Hui Wang},
        title={Research on Fresh Produce Sales Prediction Algorithm for Store Based on Multidimensional Time Series Data Analysis},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={2},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={10},
        keywords={Fresh produce sales prediction, Multidimensional time series data, Combined prediction model, LSTM},
        doi={10.4108/eetsis.3844}
    }
    
  • Zhiyu Li
    Wei Yu
    Wenwei Zhu
    Haojie Wan
    Jingjing Peng
    Hui Wang
    Year: 2023
    Research on Fresh Produce Sales Prediction Algorithm for Store Based on Multidimensional Time Series Data Analysis
    SIS
    EAI
    DOI: 10.4108/eetsis.3844
Zhiyu Li1, Wei Yu1,*, Wenwei Zhu1, Haojie Wan1, Jingjing Peng1, Hui Wang2
  • 1: Wuhan University
  • 2: Wuhan Branch of the National Science Library
*Contact email: yuwei@whu.edu.cn

Abstract

INTRODUCTION: Fresh produce is a daily necessity; however, offline stores often rely on personal experience for purchase, which is highly subjective and may result in inaccurate estimation of purchase quantities. This can lead to produce wastage and subsequently impact the profitability of business. This paper introduces a fresh produce sales prediction model, which can predict fresh produce sales by analyzing multidimensional time series data that influence sales. This model aims to provide guidance for fresh produce purchase in offline stores. OBJECTIVES: The purpose of this study is to predict fresh produce sales by analyzing multidimensional time series data that influence sales. This aims to provide a basis for fresh produce purchase in stores, reduce produce wastage, and enhance business profitability. METHODS: This paper proposes a fresh produce sales prediction model by analyzing multidimensional time series data that affect store sales of fresh produce. An essential component of this model is the ARIMA-LSTM combined prediction model. In this study, the weighted reciprocal of errors averaging method is selected as the weight determination method for the ARIMA-LSTM combined prediction model. RESULTS: In this paper, the ARIMA-LSTM combined model is used for prediction in two scenarios: when the single-model prediction accuracy is superior and when it is inferior. Experimental results indicate that in the case of lower accuracy in single-model prediction, the combined prediction model outperforms, improving prediction accuracy by 3.86% as measured by MAPE. Comparative experiments are conducted between the fresh produce sales prediction model proposed in this paper and time series prediction framework Prophet, traditional LSTM model, and ARIMA model. The experimental results indicate that the proposed model outperforms the others. CONCLUSION: The fresh produce sales prediction model proposed in this paper is based on multidimensional time series data to predict fresh produce sales in stores. This model can accurately predict the sales of fresh produce, providing purchase guidance for fresh produce stores, reducing fresh produce wastage caused by subjective purchasing factors, and increase business profits.

Keywords
Fresh produce sales prediction, Multidimensional time series data, Combined prediction model, LSTM
Received
2023-09-15
Accepted
2023-10-19
Published
2023-10-20
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.3844

Copyright © 2023 Z. Li et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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