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

1,2, 1,2,3,4 and 1,2,*
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
Author to whom correspondence should be addressed.
Received: 21 December 2017 / Revised: 25 January 2018 / Accepted: 31 January 2018 / Published: 5 February 2018
PDF [481 KB, uploaded 6 February 2018]


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. View Full-Text
Keywords: empirical mode decomposition; support vector regression; forecasting empirical mode decomposition; support vector regression; forecasting

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Nava, N.; Di Matteo, T.; Aste, T. Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression. Risks 2018, 6, 7.

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