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

Oregi, IzaskunPérez, AritzDel Ser, JavierLozano, Jose A.2022-08Oregi , 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/s00521-022-07358-30941-0643researchoutputwizard: 11556/1365researchoutputwizard: 11556/1364Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.In 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.16enginfo:eu-repo/semantics/openAccessAn active adaptation strategy for streaming time series classification based on elastic similarity measuresjournal article10.1007/s00521-022-07358-3Deep learningDynamic time warpingStreaming dataTime series classificationTime series classificationStreaming dataDeep learningDynamic time warpingSoftwareArtificial IntelligenceFunding InfoFunding 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)Funding 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)Funding 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)Funding 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)http://www.scopus.com/inward/record.url?scp=85130288331&partnerID=8YFLogxK