Revenue Prediction of Supermarket Vegetable Commodities Based on LSTM Neural Network
In today's highly competitive market environment,accurate sales forecasting is crucial for the efficient operation of supermarkets,especially in the field of vegetable commodities.To optimize inventory control of vegetable commodities in supermarkets,it is essential to accurately predict the demand for these products.This study utilizes the LSTM neural network to analyze and forecast historical sales data based on time series,predicting the total sales volume,wholesale prices,and unit selling prices of vegetable commodities.The approach further refines predictions for categorized vegetable commodities and individual items,forecasting replenishment quantities and pricing strategies for each item within the categorized commodities.The objective is to cal-culate the maximum revenue for supermarkets,providing supermarket management with more precise inventory management decisions for procurement and sales strategies.This,in turn,serves as a reliable basis for maximizing sales profit in supermarkets.