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Model-Based Time Series Classification
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2014 (English)In: Advances in Intelligent Data Analysis XIII / [ed] Blockeel, H; VanLeeuwen, M; Vinciotti, V, Springer, 2014, 179-191 p.Conference paper (Refereed)
Abstract [en]

We propose MTSC, a filter-and-refine framework for time series Nearest Neighbor (NN) classification. Training time series belonging to certain classes are first modeled through Hidden Markov Models (HMMs). Given an unlabeled query, and at the filter step, we identify the top K models that have most likely produced the query. At the refine step, a distance measure is applied between the query and all training time series of the top K models. The query is then assigned with the class of the NN. In our experiments, we first evaluated the NN classification error rate of HMMs compared to three state-of-the-art distance measures on 45 time series datasets of the UCR archive, and showed that modeling time series with HMMs achieves lower error rates in 30 datasets and equal error rates in 4. Secondly, we compared MTSC with Cross Validation defined over the three measures on 33 datasets, and we observed that MTSC is at least as good as the competitor method in 23 datasets, while achieving competitive speedups, showing its effectiveness and efficiency.

Place, publisher, year, edition, pages
Springer, 2014. 179-191 p.
, Lecture notes in computer science, ISSN 1611-3349 ; 8819
National Category
Information Systems
Research subject
Computer and Systems Sciences
URN: urn:nbn:se:su:diva-108640DOI: 10.1007/978-3-319-12571-8_16ISI: 000350861600016ISBN: 978-3-319-12571-8ISBN: 978-3-319-12570-1OAI: diva2:759833
3th International Symposium, IDA 2014, Leuven, Belgium, October 30 - November 1, 2014
Available from: 2014-10-31 Created: 2014-10-31 Last updated: 2015-04-23Bibliographically approved

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Papapetrou, Panagiotis
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