Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost

Stock price forecasting is an important issue and interesting topic in financial markets. Because reasonable and accurate forecasts have the potential to generate high economic benefits, many researchers have been involved in the study of stock price forecasts. In this paper, the DWT-ARIMA-GSXGB hybrid model is proposed. Firstly, the discrete wavelet transform is used to split the data set into approximation and error parts. Then the ARIMA (0, 1, 1), ARIMA (1, 1, 0), ARIMA (2, 1, 1) and ARIMA (3, 1, 0) models respectively process approximate partial data and the improved xgboost model (GSXGB) handles error partial data. Finally, the prediction results are combined using wavelet reconstruction. According to the experimental comparison of 10 stock data sets, it is found that the errors of DWT-ARIMA-GSXGB model are less than the four prediction models of ARIMA, XGBoost, GSXGB and DWT-ARI-MA-XGBoost. The simulation results show that the DWT-ARIMA-GSXGB stock price prediction model has good approximation ability and generalization ability, and can fit the stock index opening price well. And the proposed model is considered to greatly improve the predictive performance of a single ARIMA model or a single XGBoost model in predicting stock prices.