Research On Vegetable Replenishment and Pricing Based on LSTM Neural Network Prediction Modeling

Abstract

Vegetables in fresh food supermarkets face challenges with pricing and replenishment since the majority of vegetable varieties have a short shelf life. Fresh food supermarkets must replenish goods based on sales history and demand. In this paper, the pricing and replenishment decisions of fresh supermarkets are predicted using the random forest and LSTM neural network prediction models. The findings indicate that the prediction model in this paper can have a good predictive effect and that the decisions made by fresh supermarkets regarding pricing and replenishment are related to the historical time series data. This paper's study can assist in resolving price and replenishment issues in fresh food stores, which will have some positive economic effects.