The Canonical Interval Forest {(CIF)} Classifier for Time Series Classification

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Middlehurst, Matthew, Large, James and Bagnall, Anthony (2021) The Canonical Interval Forest {(CIF)} Classifier for Time Series Classification. In: Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. The Institute of Electrical and Electronics Engineers (IEEE), USA, pp. 188-195. ISBN 978-1-7281-6252-2

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Abstract

Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) classifier is one of the most well known interval methods, and has demonstrated strong performance as well as relative speed in training and predictions. However, recent advances in other approaches have left TSF behind. TSF originally summarises intervals using three simple summary statistics. The `catch22' feature set of 22 time series features was recently proposed to aid time series analysis through a concise set of diverse and informative descriptive characteristics. We propose combining TSF and catch22 to form a new classifier, the Canonical Interval Forest (CIF). We outline additional enhancements to the training procedure, and extend the classifier to include multivariate classification capabilities. We demonstrate a large and significant improvement in accuracy over both TSF and catch22, and show it to be on par with top performers from other algorithmic classes. By upgrading the interval-based component from TSF to CIF, we also demonstrate a significant improvement in the hierarchical vote collective of transformation-based ensembles (HIVE-COTE) that combines different time series representations. HIVE-COTE using CIF is significantly more accurate on the UCR archive than any other classifier we are aware of and represents a new state of the art for TSC.

Item Type:	Book Section
Additional Information:	Funding: This work is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) iCASE award T206188 sponsored by British Telecom.
Uncontrolled Keywords:	classification,ensembles,hive-cote,multivariate,time series,computer networks and communications,information systems,information systems and management,safety, risk, reliability and quality ,/dk/atira/pure/subjectarea/asjc/1700/1705
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and Statistics
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	27 Mar 2021 00:57
Last Modified:	08 Mar 2024 11:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/79575
DOI:	10.1109/BigData50022.2020.9378424

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