Article
Forecasts for international financial series with VMD algorithms
Journal of Asian Economics
(2022)
Abstract
Recent models with variational mode decomposition (VMD) have been applied to time-series forecasting. In this paper, we build a hybrid model named VMD–autoregressive integrated moving average (ARIMA)–Taylor expansion forecasting (TEF) to increase accuracy and stability for predicting financial time series. We use VMD algorithms to decompose financial series into subseries. An ARIMA model is built to predict each mode’s linear component, and the pragmatic TEF model based on a tracking differentiator is applied to forecast of the nonlinear component. Then the forecasts of all subseries are assembled as a final forecast. Our empirical results of international stock indices demonstrate that the proposed hybrid approach surpasses several existing state-of-the-art hybrid models.
Keywords
- International stock indices,
- Forecasting,
- Variational mode decomposition,
- Taylor expansion forecasting,
- ARIMA,
- Financial time series
Disciplines
Publication Date
June, 2022
DOI
10.1016/j.asieco.2022.101458
Citation Information
Yiuman Tse and Wei Guo. "Forecasts for international financial series with VMD algorithms" Journal of Asian Economics Vol. 80 (2022) Available at: http://works.bepress.com/yiuman-tse/144/