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Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression

Author

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  • Noemi Nava

    (Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
    Systemic Risk Centre, London School of Economics and Political Sciences, London WC2A2AE, UK)

  • Tiziana Di Matteo

    (Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
    Systemic Risk Centre, London School of Economics and Political Sciences, London WC2A2AE, UK
    Department of Mathematics, King’s College London, The Strand, London WC2R 2LS, UK
    Complexity Science Hub Vienna, Josefstaedter Strasse 39, A 1080 Vienna, Austria)

  • Tomaso Aste

    (Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
    Systemic Risk Centre, London School of Economics and Political Sciences, London WC2A2AE, UK)

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

  • Noemi Nava & Tiziana Di Matteo & Tomaso Aste, 2018. "Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression," Risks, MDPI, vol. 6(1), pages 1-21, February.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:1:p:7-:d:130251
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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.
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    7. Tim Leung & Theodore Zhao, 2021. "Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning," Papers 2105.10871, arXiv.org.
    8. Dionne, Georges & Koumou, Gilles Boevi, 2018. "Machine Learning and Risk Management: SVDD Meets RQE," Working Papers 18-6, HEC Montreal, Canada Research Chair in Risk Management.

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