LSTM-Based Time Series Prediction and Optimization of Replenishment Pricing Model

Abstract

Historical sales data play a significant role in the formulation of future replenishment and pricing schemes. In this paper, on the one hand, the correlation relationship between the sales volume of various categories and individual vegetables is investigated by data visualization and calculation of correlation coefficients in different time dimensions, and their "complementary" or "synchronous" relationships are obtained; on the other hand, a more reasonable vegetable replenishment volume and pricing scheme are predicted and obtained through the LSTM time-series prediction model and linear programming model, which are both accurate and accurate at the same time. On the other hand, the LSTM time series prediction model and linear programming model are used to predict and obtain a more reasonable total vegetable replenishment and pricing scheme, which is accurate and flexible at the same time.