Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/195797
Authors: 
Nava, Noemi
Di Matteo, Tiziana
Aste, Tomaso
Year of Publication: 
2018
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 6 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2018 [Pages:] 1-21
Publisher: 
MDPI, Basel
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.
Subjects: 
empirical mode decomposition
support vector regression
forecasting
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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