Research on Vegetable Pricing and Replenishment Based on LSTM Time Series Prediction Method

In the operation of fresh supermarkets, vegetable products exhibit significant price volatility compared to other commodities due to their perishability, freshness, and susceptibility to losses. This study utilizes historical sales and pricing data from supermarkets and employs the LSTM neural network forecasting model to conduct predictive analysis on the replenishment decisions and pricing strategies in fresh supermarkets. The results demonstrate that the proposed forecasting model achieves favorable predictive accuracy, revealing a certain correlation and regularity between the replenishment decisions in fresh supermarkets and historical time series data. This research offers positive economic benefits in guiding the variations of prices and replenishment quantities.