Benchmarking Univariate Time Series Classifiers
Abstract
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.
- Citation
- BibTeX
Schäfer, P. & Leser, U.,
(2017).
Benchmarking Univariate Time Series Classifiers.
In:
Mitschang, B., Nicklas, D., Leymann, F., Schöning, H., Herschel, M., Teubner, J., Härder, T., Kopp, O. & Wieland, M.
(Hrsg.),
Datenbanksysteme für Business, Technologie und Web (BTW 2017).
Gesellschaft für Informatik, Bonn.
(S. 289-298).
@inproceedings{mci/Schäfer2017,
author = {Schäfer, Patrick AND Leser, Ulf},
title = {Benchmarking Univariate Time Series Classifiers},
booktitle = {Datenbanksysteme für Business, Technologie und Web (BTW 2017)},
year = {2017},
editor = {Mitschang, Bernhard AND Nicklas, Daniela AND Leymann, Frank AND Schöning, Harald AND Herschel, Melanie AND Teubner, Jens AND Härder, Theo AND Kopp, Oliver AND Wieland, Matthias} ,
pages = { 289-298 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
author = {Schäfer, Patrick AND Leser, Ulf},
title = {Benchmarking Univariate Time Series Classifiers},
booktitle = {Datenbanksysteme für Business, Technologie und Web (BTW 2017)},
year = {2017},
editor = {Mitschang, Bernhard AND Nicklas, Daniela AND Leymann, Frank AND Schöning, Harald AND Herschel, Melanie AND Teubner, Jens AND Härder, Theo AND Kopp, Oliver AND Wieland, Matthias} ,
pages = { 289-298 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
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More Info
ISBN: 978-3-88579-659-6
ISSN: 1617-5468
xmlui.MetaDataDisplay.field.date: 2017
Language:
(en)

Content Type: Text/Conference Paper