Forecasting and Analysis of Industrial Growth Based on Regression Tree Integrated Learning Methods

The paper selects 59 related economic indicators from numerous variables, and compares the forecast effects of traditional time series models with several regression tree integrated learning models on the growth rate of China’s industrial added value under different scenarios, and the SHapley Additive exPlanations (SHAP) method is combined for interpretation. Our results show that, with the increase of the forecast step and the outbreak of the COVID-19 epidemic, the forecast performance of the traditional time series model is significantly weakened, while the integrated learning model is relatively better, among which the Gradient Boosting Decision Tree model is more robust and accurate in the longer forecast step. Based on the analysis of SHAP method, we find that the importance of economic indicators as predictors in different periods is different. In addition to indicators such as production and investment, financial variables also play a certain early warning role in high-risk periods, and appropriate economic indicators should be selected according to specific time and expected goals for industrial growth forecasting and analysis. From the perspective of forecasting, the impact of the COVID-19 pandemic may not change the fundamentals of the future trend of industrial growth to some extent.