dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Arias, Marta | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-07-14T09:01:32Z | |
dc.date.available | 2016-07-14T09:01:32Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Troncoso Lora, A., Arias, M. y Riquelme Santos, J.C. (2015). A multi-scale smoothing kernel for measuring time-series similarity. Neurocomputing, 167, 8-17. | |
dc.identifier.issn | 0925-2312 | es |
dc.identifier.uri | http://hdl.handle.net/11441/43595 | |
dc.description.abstract | In this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on a
similarity or distance metric. The main idea of our kernel is that it should recognize as highly similar time-series that are
essentially the same but may be slightly perturbed from each other: for example, if one series is shifted with respect to
the other or if it slightly misaligned. Namely, our kernel tries to focus on the shape of the time-series and ignores small
perturbations such as misalignments or shifts. First, a recursive formulation of the kernel directly based on its definition
is proposed. Then it is shown how to efficiently compute the kernel using an equivalent matrix-based formulation. To
validate the proposed kernel three experiments have been carried out. As an initial step, several synthetic datasets have
been generated from UCR time-series repository and the KDD challenge of 2007 with the purpose of validating the
kernel-derived distance over shifted time-series. Also, the kernel has been applied to the original UCR time-series to
analyze its potential in time-series classification in conjunction with Support Vector Machines. Finally, two real-world
applications related to ozone concentration in atmosphere and electricity demand have been considered. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2011-27479-C04-03 | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2011-28956-C02 | es |
dc.description.sponsorship | Generalitat de Catalunya 2009-SGR-1428 | es |
dc.description.sponsorship | Junta de Andalucía P12-TIC-1728 | es |
dc.description.sponsorship | Universidad Pablo de Olavide APPB813097 | es |
dc.description.sponsorship | Unión Europea Pascal2 Network of Excellence FP7-ICT-216886 | es |
dc.description.sponsorship | Generalita de Catalunya BE-DGR2011 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Neurocomputing, 167, 8-17. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Kernel | es |
dc.subject | Similarity | es |
dc.subject | Distance | es |
dc.subject | Time-series classification | es |
dc.title | A multi-scale smoothing kernel for measuring time-series similarity | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.accessRights | info: eu-repo/semantics/embargoAccess | |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2011-27479-C04-03 | es |
dc.relation.projectID | TIN2011-28956-C02 | es |
dc.relation.projectID | 2009-SGR-1428 | es |
dc.relation.projectID | P12-TIC-1728 | es |
dc.relation.projectID | APPB813097 | es |
dc.relation.projectID | FP7-ICT-216886 | es |
dc.relation.projectID | BE-DGR2011 | es |
dc.identifier.doi | 10.1016/j.neucom.2014.08.099 | es |
idus.format.extent | 10 | es |
dc.journaltitle | Neurocomputing | es |
dc.publication.volumen | 167 | es |
dc.publication.initialPage | 8 | es |
dc.publication.endPage | 17 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/43595 | |