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

Nava, Noemi, Di Matteo, Tiziana and Aste, Tomaso (2018) Financial time series forecasting using empirical mode decomposition and support vector regression. Risks, 6 (1). ISSN 2227-9091

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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.

Item Type:	Article
Official URL:	https://www.mdpi.com/journal/risks
Additional Information:	© 2018 The Authors
Divisions:	Systemic Risk Centre
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HG Finance
JEL classification:	G - Financial Economics > G1 - General Financial Markets G - Financial Economics > G2 - Financial Institutions and Services
Date Deposited:	06 Dec 2018 10:35
Last Modified:	11 Dec 2024 21:46
URI:	http://eprints.lse.ac.uk/id/eprint/91028

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