Prediction of New Energy Vehicles via ARIMA-BP Hybrid Model

An important measure of the development of the new energy vehicle market is the prediction of vehicle sales. It is of great significance to complete the construction of relevant supporting facilities, according to the predicted sales volume for the development of the Chinese new energy vehicle industry. Based on this, this paper proposes a combined model that organically combines a single prediction model. Firstly, the ARIMA model is used to predict the linear information in the sales data, and BP neural network model is used to predict the residual sequence between the previous prediction and the actual value. After that, it adds the prediction results to get the final prediction results of new energy vehicle sales. The results verified with the actual sales data show that the prediction accuracy of the ARIMA-BP Residual Optimization Combination model used in this paper is 85.07%. Compared with the single prediction model and the simple weighted combination prediction model, there are general advantages, which can be used for the actual monthly sales prediction of new energy vehicles.