| Summary: | Industrial added value is an important indicator to measure the operation of the real economy.In order to fully mine the value of power data in the current macroeconomic nowcasting, so as to serve the government policy making, the Bagging and Boosting algorithms in machine learning were applied to nowcast industrial added value based on electric power data as well as traditional statistical data.Firstly, traditional statistical data can significantly improve the forecasting effect of the ARIMA model.Secondly, the nowcasting ability of electric power data depends on the selection of electric power indicators, and the proper electric power index is helpful to predict industrial added value more timely and accurately.Thirdly, the prediction ability of electric power data to the industrial added value in the current period may be lower than that in the future, which means the power data is more likely to be used to predict ahead of time.
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