A hybrid model for financialtime-seriesforecasting based on mixed methodologies

This paper proposes a hybrid model that combines ensemble empirical mode decomposition (EEMD), autoregressive integrated moving average (ARIMA), and Taylor expansion using a tracking differentiator to forecast financial time series. Specifically, the financial time series is decomposed by EEMD into some subseries. Then, the linear portion of each subseries is forecasted by the linear ARIMA model, while the nonlinear portion is predicted by the nonlinear Taylor expansion model. The forecasting results of the linear and nonlinear models are combined into the predicted result of each subseries. The final prediction result is obtained by combining the prediction values of all the subseries. The empirical results with real financial time series data demonstrate that this new hybrid approach outperforms the benchmark hybrid models considered in this paper.

Automated summary

This study introduces a hybrid model that combines EEMD, ARIMA, and Taylor expansion for forecasting financial time series. The empirical results demonstrate that this hybrid approach outperforms benchmark models in forecasting financial time series.