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Benchmarking Univariate Time Series Classifiers

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2017

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Gesellschaft für Informatik, Bonn

Zusammenfassung

Time 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.

Beschreibung

Schäfer, Patrick; Leser, Ulf (2017): Benchmarking Univariate Time Series Classifiers. Datenbanksysteme für Business, Technologie und Web (BTW 2017). Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-659-6. pp. 289-298. Streaming and Dataflows. Stuttgart. 6.-10. März 2017

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