Research on Vegetable Incoming and Pricing Strategies Based on Support Vector Machines and Gray Prediction Models
DOI: 10.23977/infse.2023.041018 | Downloads: 21 | Views: 775
Author(s)
Mengxiang Ding 1, Hanrui Zhang 2, Yiming Bao 3
Affiliation(s)
1 School of Finance, Shandong University of Finance and Economics, Jinan, China
2 School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China
3 School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan, China
Corresponding Author
Mengxiang DingABSTRACT
This study analyzes vegetable stocking and pricing decisions based on market demand and historical transaction data with the aim of maximizing the profit of fresh produce supermarkets. A mathematical model was constructed using descriptive statistical analysis, cluster analysis, Pearson correlation analysis, gray prediction model, entropy weight TOPSIS method and support vector machine (SVR) regression. The study performed correlation analysis on vegetable category and specific product data and found a negative correlation between similar vegetable varieties; then it used gray prediction and TOPSIS method to predict the vegetable sales volume in the coming week and combined with SVR regression model to predict the pricing. Finally, the time series model was used to develop further pricing strategies for specific high-margin vegetable items. This study provides dedicated support for scientific decision-making on vegetable sales, as well as a reference for pricing and replenishment decisions on related items.
KEYWORDS
Market Demand Analysis, Vegetable Pricing, Mathematical Modeling, Predictive AnalysisCITE THIS PAPER
Mengxiang Ding, Hanrui Zhang, Yiming Bao, Research on Vegetable Incoming and Pricing Strategies Based on Support Vector Machines and Gray Prediction Models. Information Systems and Economics (2023) Vol. 4: 134-142. DOI: http://dx.doi.org/10.23977/infse.2023.041018.
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