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Journal of Innovations in Economics & Management

Volume 5, 2024 - Issue 3

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Research on the distribution patterns and interrelationships of sales volume of various vegetable categories and individual products

by Hao Li 1,# ,  Siyi Huang 2,# ,  Rui Hu 3,# ,  Minghao Liu 4,# ,  Junhao Li 5,#  and  Fuqiang Huang 6,#
1
College of Fire Protection Engineering, China People’s Police University, Hebei, China
2
School of Mathematics and Statistics, Jishou University, Jishou, China
3
School of economics and management, Guangxi Normal University, Guilin, China
4
College of Water&Architectural Engineering, Shihezi University, Shihezi, China
5
First Clinical Medical College，Southern Medical University，Guangzhou, China
6
School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang, China
7
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*
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JIEM  2024 5(3):86; https://doi.org/10.69610/j.jiem.20240831
Received: / Accepted: / Published Online: 31 August 2024
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Abstract

This study conducts a comprehensive analysis of vegetable sales data. Using Python, the collected data was integrated, categorized, and checked for missing values with the "missingno" library. Anomalies were detected using box plots, revealing a few outliers, which were retained as they more accurately reflect real business phenomena. The analysis explored distribution patterns and interrelationships of sales quantities across different vegetable categories and individual items. Time series decomposition revealed significant seasonal variations in sales volumes throughout the year. Model accuracy was validated through residual analysis, and missing values were imputed using the Prophet model. Dynamic Time Warping (DTW) calculated distance matrices between categories to uncover similarities. K-means clustering analyzed sales trends and seasonal patterns of individual items, with DTW providing detailed similarity analysis within clusters. This approach identified correlated sales trends, such as the high correlation between Chinese cabbage and bell peppers, indicating that consumers may prefer to purchase these vegetables together. These findings offer valuable insights for optimizing supermarket restocking strategies.

Keywords: Vegetable Sales Data; Python; Missing Values; Anomaly Detection; Box Plots; Time Series Decomposition; Prophet Model; Dynamic Time Warping (DTW); K-means Clustering; Sales Trends; Seasonal Patterns; Supermarket Restocking;
Copyright: © 2024 by Li, Huang, Hu, Liu, Li and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
Li, H.; Huang, S.; Hu, R.; Liu, M.; Li, J.; Huang, F. Research on the distribution patterns and interrelationships of sales volume of various vegetable categories and individual products. Journal of Innovations in Economics & Management, 2024, 5, 86. doi:10.69610/j.jiem.20240831
AMA Style
Li H, Huang S, Hu R et al.. Research on the distribution patterns and interrelationships of sales volume of various vegetable categories and individual products. Journal of Innovations in Economics & Management; 2024, 5(3):86. doi:10.69610/j.jiem.20240831
Chicago/Turabian Style
Li, Hao; Huang, Siyi; Hu, Rui; Liu, Minghao; Li, Junhao; Huang, Fuqiang 2024. "Research on the distribution patterns and interrelationships of sales volume of various vegetable categories and individual products" Journal of Innovations in Economics & Management 5, no.3:86. doi:10.69610/j.jiem.20240831

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