Optimal Pricing and Replenishment Strategy of Goods by Using ARIMA Time Series Prediction Model

This paper investigates the sales volume distribution, pricing and replenishment volume of vegetable com-modities. First, through descriptive statistical analysis of the data, the distribution pattern of sales volume of each veg-etable commodity category is obtained. Then, through the Pearson correlation test, the relationship between the sales volume of each category is obtained, and the least squares method is used to conduct linear regression analysis to obtain the specific correlation function between each vegetable category. Next, the relationship between sales volume and selling price is explored through nonlinear fitting. Using the ARIMA time series prediction model, a dynamic customer demand function is introduced based on the principle of optimal supply-demand balance and benefits. An optimization model that maximizes supermarket profits is established, and the pricing and replenishment strategies for the next week are predicted.