Research on Pricing Strategy of Fresh Vegetable Commodities in Grocery Stores Based on Linear Regression and ARIMA Model

This study explores the pricing strategy for fresh vegetable commodities in grocery stores using linear regression and ARIMA models. After preprocessing the data and excluding return transactions, Spearman correlation analysis revealed a significant negative correlation between total vegetable sales volume and cost-plus pricing. Further linear regression analysis confirmed this negative linear relationship, demonstrating the model's statistical significance. Subsequently, the ARIMA model was employed to analyze time series data, forecast future replenishment volumes, and pricing strategies to maximize supermarket profits. The ARIMA model captures trends and seasonality in the data through autocorrelation analysis, providing more accurate predictions for vegetable product pricing and replenishment. In the experimental section, stationarity of the time series was ensured through first-order differencing and ADF testing, and ARIMA model parameters were determined using ACF and PACF plots. Finally, the optimized model was used to forecast the replenishment volume and pricing strategy for July 1-7, 2023. The study concludes that the ARIMA model is an effective tool for supermarket managers to make data-driven decisions.