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

Nava, N; Di Matteo, T; Aste, T; (2018) Financial time series forecasting using empirical mode decomposition and support vector regression. Risks , 6 (1) , Article 7. 10.3390/risks6010007. Green open access

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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.

Type: Article
Title: Financial time series forecasting using empirical mode decomposition and support vector regression
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/risks6010007
Publisher version: https://doi.org/10.3390/risks6010007
Language: English
Additional information: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: empirical mode decomposition; support vector regression; forecasting
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10063277
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