Prediction Method of Short-Term Demand for e-Commerce Goods Based on Deep Neural Network

Abstract

In order to improve the short-term demand prediction effect of e-commerce commodities, this paper combines the deep neural network algorithm to predict the short-term demand of e-commerce commodities and proposes a nonparametric supply chain demand prediction model based on multilayer Bayesian network. Moreover, this paper uses the hidden layer factor to describe the internal relationship of customer demand in time series and uses the bottom layer factor to represent the actual customer demand. In addition, this paper directly takes side information into consideration to improve the accuracy of customer demand prediction. Through simulation experiments, it can be seen that the prediction method of short-term demand for e-commerce goods based on deep neural network is close to the actual demand, and it can play a certain role in the demand prediction of e-commerce goods.