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

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

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

Noemi Nava, Tiziana Di Matteo and Tomaso Aste

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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: G1 G2 (search for similar items in EconPapers)
Date: 2018-02-05
New Economics Papers: this item is included in nep-ecm and nep-for
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Citations: View citations in EconPapers (8)

Published in Risks, 5, February, 2018, 6(1). ISSN: 2227-9091

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:91028

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