Predicting future sales of vegetable category based on grid search optimized long and short term neural networks

Abstract

 In this study, a method of automatic prediction of vegetable sales volume was proposed by using long and short term neural network (LSTM) to solve the problem of quantity judgment in vegetable supply chain management. This text constructed a trained and optimized LSTM forecasting model by collecting historical sales data and selling and wholesale prices of vegetable markets. After the optimal parameters were determined by grid search, the model underwent 10 training cycles. The model evaluation for each vegetable category led to the following conclusion: the model performed well in predicting sales. By using the evaluation metrics of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), it was found that the model predictions were close to the true values. In addition,  the prediction results are summarized, showing the model's accurate prediction of vegetable sales volume. The results of the comparison chart between the real and predicted sales volume of each category of vegetables show that the model has good performance and accuracy in predicting the sales volume of vegetables, and the accurate forecast quantity can provide supply chain managers with more accurate inventory replenishment and pricing strategies.