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Forecasts for international financial series with VMD algorithms

Author

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  • Guo, Wei
  • Liu, Qingfu
  • Luo, Zhidan
  • Tse, Yiuman

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.

Suggested Citation

  • Guo, Wei & Liu, Qingfu & Luo, Zhidan & Tse, Yiuman, 2022. "Forecasts for international financial series with VMD algorithms," Journal of Asian Economics, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:asieco:v:80:y:2022:i:c:s1049007822000185
    DOI: 10.1016/j.asieco.2022.101458
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    2. Xing, Zhuoqun & Pan, Yiqun & Yang, Yiting & Yuan, Xiaolei & Liang, Yumin & Huang, Zhizhong, 2024. "Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction," Applied Energy, Elsevier, vol. 365(C).
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    4. Wang, Xiangning & Huang, Qian & Zhang, Shuguang, 2023. "Effects of macroeconomic factors on stock prices for BRICS using the variational mode decomposition and quantile method," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    5. Hamid Nasiri & Mohammad Mehdi Ebadzadeh, 2022. "Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition," Papers 2212.14687, arXiv.org.

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