An active adaptation strategy for streaming time series classification based on elastic similarity measures

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dc.contributor.authorOregi, Izaskun
dc.contributor.authorPérez, Aritz
dc.contributor.authorDel Ser, Javier
dc.contributor.authorLozano, Jose A.
dc.contributor.institutionQuantum
dc.contributor.institutionIA
dc.date.issued2022-08
dc.descriptionPublisher Copyright: © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
dc.description.abstractIn streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line scenarios, data arise from non-stationary processes, which results in a succession of different patterns or events. This work presents an active adaptation strategy that allows time series classifiers to accommodate to the dynamics of streamed time series data. Specifically, our approach consists of a classifier that detects changes between events over streaming time series. For this purpose, the classifier uses features of the dynamic time warping measure computed between the streamed data and a set of reference patterns. When classifying a streaming series, the proposed pattern end detector analyzes such features to predict changes and adapt off-line time series classifiers to newly arriving events. To evaluate the performance of the proposed scheme, we employ the pattern end detection model along with dynamic time warping-based nearest neighbor classifiers over a benchmark of ten time series classification problems. The obtained results present exciting insights into the detection accuracy and latency performance of the proposed strategy.en
dc.description.sponsorshipThis research work has been supported by the Basque Government through the EMAITEK and ELKARTEK funding programs (3KIA, ref. KK-2020/00049), as well as by the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of this institution. I. Oregi and J. Del Ser would like to thank the Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation) through the “Red Cervera” Programme (AI4ES project). A. Pérez and J. A. Lozano are supported by the Basque Government through the BERC 2022-2025 program and by the Spanish Ministry of Economy and Competitiveness MINECO through BCAM Severo Ochoa excellence accreditation SEV-2017-0718. A. Pérez also acknowledges funding support from AEI/FEDER (UE) through project TIN2017-82626-R. J. A. Lozano is also supported by Spanish Ministry of Economy and Competitiveness MINECO through TIN2016-78365-R and PID2019-104966GB-I00.
dc.description.statusPeer reviewed
dc.format.extent16
dc.identifier.citationOregi , I , Pérez , A , Del Ser , J & Lozano , J A 2022 , ' An active adaptation strategy for streaming time series classification based on elastic similarity measures ' , Neural Computing and Applications , vol. 34 , no. 16 , pp. 13237-13252 . https://doi.org/10.1007/s00521-022-07358-3
dc.identifier.doi10.1007/s00521-022-07358-3
dc.identifier.issn0941-0643
dc.identifier.otherresearchoutputwizard: 11556/1365
dc.identifier.otherresearchoutputwizard: 11556/1364
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85130288331&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofNeural Computing and Applications
dc.relation.projectIDBERC, 2022-2025
dc.relation.projectIDU.S. Department of Education, ED
dc.relation.projectIDCentro para el Desarrollo Tecnológico Industrial, CDTI
dc.relation.projectIDFederación Española de Enfermedades Raras, FEDER, TIN2017-82626-R-PID2019-104966GB-I00-TIN2016-78365-R
dc.relation.projectIDEusko Jaurlaritza, KK-2020/00049-IT1294-19
dc.relation.projectIDMinisterio de Economía y Competitividad, MINECO
dc.relation.projectIDMinisterio de Ciencia e Innovación, MICINN
dc.relation.projectIDAgencia Estatal de Investigación, AEI
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsDeep learning
dc.subject.keywordsDynamic time warping
dc.subject.keywordsStreaming data
dc.subject.keywordsTime series classification
dc.subject.keywordsTime series classification
dc.subject.keywordsStreaming data
dc.subject.keywordsDeep learning
dc.subject.keywordsDynamic time warping
dc.subject.keywordsSoftware
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsFunding Info
dc.subject.keywordsFunding was provided by Eusko Jaurlaritza (KK-2020/00049, MATHMODE (IT1294-19), BERC 2022-2025), _x000D_ Ministerio de Economı´a y Competitividad (Severo Ochoa SEV-2017-0718,TIN2016-78365-R), _x000D_ Agencia Estatal de Investigacio´n (TIN2017-82626-R)
dc.subject.keywordsFunding was provided by Eusko Jaurlaritza (KK-2020/00049, MATHMODE (IT1294-19), _x000D_ BERC 2022-2025), Ministerio de Economía y Competitividad (Severo Ochoa SEV-2017-0718, TIN2016-78365-R), _x000D_ Agencia Estatal de Investigación (TIN2017-82626-R)
dc.subject.keywordsFunding was provided by Eusko Jaurlaritza (KK-2020/00049, MATHMODE (IT1294-19), BERC 2022-2025), _x000D_ Ministerio de Economı´a y Competitividad (Severo Ochoa SEV-2017-0718,TIN2016-78365-R), _x000D_ Agencia Estatal de Investigacio´n (TIN2017-82626-R)
dc.subject.keywordsFunding was provided by Eusko Jaurlaritza (KK-2020/00049, MATHMODE (IT1294-19), _x000D_ BERC 2022-2025), Ministerio de Economía y Competitividad (Severo Ochoa SEV-2017-0718, TIN2016-78365-R), _x000D_ Agencia Estatal de Investigación (TIN2017-82626-R)
dc.titleAn active adaptation strategy for streaming time series classification based on elastic similarity measuresen
dc.typejournal article
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