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Model-Based Time Series Classification

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Advances in Intelligent Data Analysis XIII (IDA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8819))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Texas at Arlington, USA

    Alexios Kotsifakos

  2. Department of Computer and Systems Sciences, Stockholm University, Sweden

    Panagiotis Papapetrou

Authors
  1. Alexios Kotsifakos
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  2. Panagiotis Papapetrou
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Editor information

Editors and Affiliations

  1. Department of Computer Science, KU Leuven, 3001, Heverlee, Belgium

    Hendrik Blockeel  & Matthijs van Leeuwen  & 

  2. Brunel University, UB8 3PH, Uxbridge, UK

    Veronica Vinciotti

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Kotsifakos, A., Papapetrou, P. (2014). Model-Based Time Series Classification. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds) Advances in Intelligent Data Analysis XIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer, Cham. https://doi.org/10.1007/978-3-319-12571-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-12571-8_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12570-1

  • Online ISBN: 978-3-319-12571-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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