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Financial time series forecasting using empirical mode decomposition and support vector regression

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  • Nava, Noemi
  • Di Matteo, Tiziana
  • Aste, Tomaso

Abstract

We introduce a multistep-ahead forecasting methodology that combines empirical mode decomposition (EMD) and support vector regression (SVR). This methodology is based on the idea that the forecasting task is simplified by using as input for SVR the time series decomposed with EMD. The outcomes of this methodology are compared with benchmark models commonly used in the literature. The results demonstrate that the combination of EMD and SVR can outperform benchmark models significantly, predicting the Standard & Poor’s 500 Index from 30 s to 25 min ahead. The high-frequency components better forecast short-term horizons, whereas the low-frequency components better forecast long-term horizons.

Suggested Citation

  • Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2018. "Financial time series forecasting using empirical mode decomposition and support vector regression," LSE Research Online Documents on Economics 91028, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:91028
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    File URL: http://eprints.lse.ac.uk/91028/
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    References listed on IDEAS

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    1. Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2016. "Time-dependent scaling patterns in high frequency financial data," LSE Research Online Documents on Economics 68645, London School of Economics and Political Science, LSE Library.
    2. Aymanns, Christoph & Caccioli, Fabio & Farmer, J. Doyne & Tan, Vincent W.C., 2016. "Taming the Basel leverage cycle," Journal of Financial Stability, Elsevier, vol. 27(C), pages 263-277.
    3. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
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    5. Cheng, Ching-Hsue & Wei, Liang-Ying, 2014. "A novel time-series model based on empirical mode decomposition for forecasting TAIEX," Economic Modelling, Elsevier, vol. 36(C), pages 136-141.
    6. Varga-Haszonits, Istvan & Caccioli, Fabio & Kondor, Imre, 2016. "Replica approach to mean-variance portfolio optimization," LSE Research Online Documents on Economics 68955, London School of Economics and Political Science, LSE Library.
    7. Qingcheng Zeng & Chenrui Qu, 2014. "An approach for Baltic Dry Index analysis based on empirical mode decomposition," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(3), pages 224-240, May.
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    9. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
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    11. T. Di Matteo, 2007. "Multi-scaling in finance," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 21-36.
    12. Istvan Varga-Haszonits & Fabio Caccioli & Imre Kondor, 2016. "Replica approach to mean-variance portfolio optimization," Papers 1606.08679, arXiv.org.
    13. Lin, Chiun-Sin & Chiu, Sheng-Hsiung & Lin, Tzu-Yu, 2012. "Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting," Economic Modelling, Elsevier, vol. 29(6), pages 2583-2590.
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    15. Noemi Nava & T. Di Matteo & Tomaso Aste, 2017. "Dynamic correlations at different time-scales with Empirical Mode Decomposition," Papers 1708.06586, arXiv.org.
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    More about this item

    Keywords

    empirical mode decomposition; support vector regression; forecasting;

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services

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