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Multivariate dynamic kernels for financial time series forecasting
dc.contributor.author | Peña Grass, Mauricio |
dc.contributor.author | Arratia Quesada, Argimiro Alejandro |
dc.contributor.author | Belanche Muñoz, Luis Antonio |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2017-03-09T08:50:56Z |
dc.date.available | 2017-03-09T08:50:56Z |
dc.date.issued | 2016 |
dc.identifier.citation | Peña, M., Arratia, A., Belanche, L. Multivariate dynamic kernels for financial time series forecasting. A: International Conference on Artificial Neural Networks. "Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, proceedings, part II". Barcelona: Springer, 2016, p. 336-344. |
dc.identifier.isbn | 978-3-319-44777-3 |
dc.identifier.uri | http://hdl.handle.net/2117/102167 |
dc.description | The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44781-0_40 |
dc.description.abstract | 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. |
dc.format.extent | 9 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
dc.subject.lcsh | Statistics -- Applications |
dc.subject.other | Support vector regression |
dc.subject.other | Financial time series |
dc.subject.other | Kernels |
dc.title | Multivariate dynamic kernels for financial time series forecasting |
dc.type | Conference report |
dc.subject.lemac | Estadística matemàtica -- Aplicacions |
dc.contributor.group | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.identifier.doi | 10.1007/978-3-319-44781-0_40 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.ams | Classificació AMS::62 Statistics::62P Applications |
dc.relation.publisherversion | http://link.springer.com/chapter/10.1007/978-3-319-44781-0_40 |
dc.rights.access | Open Access |
local.identifier.drac | 19739047 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Peña, M.; Arratia, A.; Belanche, Ll. |
local.citation.contributor | International Conference on Artificial Neural Networks |
local.citation.pubplace | Barcelona |
local.citation.publicationName | Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, proceedings, part II |
local.citation.startingPage | 336 |
local.citation.endingPage | 344 |