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Benchmarking Univariate Time Series Classifiers

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dc.contributor.authorSchäfer, Patrick
dc.contributor.authorLeser, Ulf
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorNicklas, Daniela
dc.contributor.editorLeymann, Frank
dc.contributor.editorSchöning, Harald
dc.contributor.editorHerschel, Melanie
dc.contributor.editorTeubner, Jens
dc.contributor.editorHärder, Theo
dc.contributor.editorKopp, Oliver
dc.contributor.editorWieland, Matthias
dc.date.accessioned2017-06-20T20:24:29Z
dc.date.available2017-06-20T20:24:29Z
dc.date.issued2017
dc.description.abstractTime series are a collection of values sequentially recorded over time. Nowadays, sensors for recording time series are omnipresent as RFID chips, wearables, smart homes, or event-based systems. Time series classification aims at predicting a class label for a time series whose label is unknown. Therefore, a classifier has to train a model using labeled samples. Classification time is a key challenge given new applications like event-based monitoring, real-time decision or streaming systems. This paper is the first benchmark that compares 12 state of the art time series classifiers based on prediction and classification times. We observed that most of the state-of-the-art classifiers require extensive train and classification times, and might not be applicable for these new applications.en
dc.identifier.isbn978-3-88579-659-6
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2017)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-265
dc.subjectBenchmark
dc.subjectTime Series
dc.subjectClassification
dc.subjectScalability
dc.titleBenchmarking Univariate Time Series Classifiersen
dc.typeText/Conference Paper
gi.citation.endPage298
gi.citation.startPage289
gi.conference.date6.-10. März 2017
gi.conference.locationStuttgart
gi.conference.sessiontitleStreaming and Dataflows

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