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

Noemi Nava, Tiziana Di Matteo and Tomaso Aste
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Noemi Nava: Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
Tiziana Di Matteo: Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
Tomaso Aste: Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK

Risks, 2018, vol. 6, issue 1, 1-21

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.

Keywords: empirical mode decomposition; support vector regression; forecasting (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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