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Title:
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Multivariate dynamic kernels for financial time series forecasting
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Author:
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Peña, Mauricio; Arratia Quesada, Argimiro Alejandro; Belanche Muñoz, Luis Antonio
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
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Abstract:
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The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44781-0_40 |
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Abstract:
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We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done through multivariate dynamic kernels within support vector regression. We also propose two kernels for financial time series that are computationally efficient without a sacrifice on accuracy. The efficacy of the methodology is demonstrated by empirical experiments on forecasting the challenging S&P500 market. |
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Abstract:
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Peer Reviewed |
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Subject(s):
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-Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
-Statistics -- Applications
-Support vector regression
-Financial time series
-Kernels
-Estadística matemàtica--Aplicacions
-Classificació AMS::62 Statistics::62P Applications |
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Rights:
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Document type:
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Article - Submitted version
Conference Object |
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Published by:
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Springer
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Share:
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