Research on Sales Decision of Vegetable Products Based on Time Series Analysis

This article studies the automatic pricing and replenishment decision-making of vegetable products. Through factor analysis and time series analysis, analyze the inherent laws, and combine mathematical programming with genetic algorithm to make sales decisions for vegetable products. Firstly, the data is preprocessed using time series analysis to predict and fill in missing values from September 12, 2022 to October 14, 2022, when the data is empty. Secondly, the relationship between vegetable types was analyzed. JB and Q_Q tests were conducted on the daily sales of each category, and it was found that the data did not follow a normal distribution. Therefore, this article uses Spearman correlation coefficient to analyze the correlation between different categories and finds that the flavor, taste, or cooking characteristics of edible mushrooms and water rhizomes are complementary and can be used in combination. To analyze the relationship between the sales volume and price of vegetable varieties, this article uses linear regression analysis to find that there is a negative correlation between sales volume and price. Finally, considering the shortcomings of time series analysis and factor analysis models, combined with practical considerations, the data to be collected includes competitor data, seasonal sales data, market trends and consumer insights data, inventory data, weather data, promotion and discount data, purchasing power data, customer feedback and demand data, vegetable supply chain data, and loss rate data.