Clothing Sales Forecast Based on ARIMA-BP Neural Network Combination Model

Clothing sales forecast can help clothing producers and sellers to make reasonable plans. Considering that there are linear and non-linear factors in the clothing sales data, it is inevitable to lose part of the data information if a single prediction model is used, resulting in a large error in the prediction results. Among the prediction methods, ARIMA model has an ideal prediction effect on linear time series, while BP neural network has a good prediction effect on nonlinear time series. In order to predict clothing sales accurately and efficiently, a prediction model based on the weighted combination of ARIAM-BP neural network is proposed. By using MATLAB software to simulate the clothing sales data, the results show that the prediction accuracy of ARIMA-BP neural network combination model is better than that of a single model.