Artikel
Financial time series forecasting using empirical mode decomposition and support vector regression
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Risks ; ISSN: 2227-9091 ; Volume: 6 ; Year: 2018 ; Issue: 1 ; Pages: 1-21 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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empirical mode decomposition
support vector regression
forecasting
- Event
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Geistige Schöpfung
- (who)
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Nava, Noemi
Di Matteo, Tiziana
Aste, Tomaso
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2018
- DOI
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doi:10.3390/risks6010007
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Nava, Noemi
- Di Matteo, Tiziana
- Aste, Tomaso
- MDPI
Time of origin
- 2018
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