Forecasts for international financial series with VMD algorithms

Wei Guo, Qingfu Liu, Zhidan Luo and Yiuman Tse

Journal of Asian Economics, 2022, vol. 80, issue C

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 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:asieco:v:80:y:2022:i:c:s1049007822000185

DOI: 10.1016/j.asieco.2022.101458

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