Research on the Variable Weight Combined Prediction Based on the Optimization Weighting Method

Abstract

This paper improves fixed weight combined prediction model, which is established on the predicted value of the ARIMA model and the predicted value of the BP neural network model. Based on the minimum sum of error squares, the time varying optimal weight of the combined prediction model is determined, variable weight combined prediction model is constructed. Based on the analysis of the change rule of Shaanxi province’s GDP over the years, the combined model is used to fit Shaanxi province GDP from 2018 to 2021.The result shows that the fitting errors are 0.11%, 0.01, 0.01% and 0.11%, respectively. The variable weight combined model is used to predict Shaanxi province GDP from 2022 to 2023, which is 29502 billion and 31301 billion respectively. Compared with the ARIMA and BP models, variable weight combined model improves the prediction effect.