Abstract
Classifying multivariate time series is often dealt with by transforming the numeric series into labelled intervals, because many pattern representations exist to deal with labelled intervals. Finding the right preprocessing is not only time consuming but also critical for the success of the learning algorithms. In this paper we show how pattern graphs, a powerful pattern language for temporal classification rules, can be extended in order to handle labelled intervals in combination with the raw time series. We thereby reduce dependence on the quality of the preprocessing and at the same time increase performance. These benefits are demonstrated experimentally on 10 different data sets.
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Notes
- 1.
shortest in the following sense: \(\forall s' \in Q': \lnot \exists s \in Q: s \subset s'\).
- 2.
Keogh, E., Zhu, Q., Hu, B., Hao. Y., Xi, X., Wei, L. & Ratanamahatana, C. A. (2011). The UCR Time Series Classification/Clustering Homepage: www.cs.ucr.edu/~eamonn/time_series_data/.
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Acknowledgments
We would like to thank Stefan Mock from the Robert Bosch GmbH for kindly providing the data.
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Peter, S., Höppner, F., Berthold, M.R. (2013). Pattern Graphs: Combining Multivariate Time Series and Labelled Interval Sequences for Classification. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_1
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DOI: https://doi.org/10.1007/978-3-319-02621-3_1
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