Abstract
We suggest a simple yet effective and parameter-free feature construction process for time series classification. Our process is decomposed in three steps: (i) we transform original data into several simple representations; (ii) on each representation, we apply a coclustering method; (iii) we use coclustering results to build new features for time series. It results in a new transactional (i.e. object-attribute oriented) data set, made of time series identifiers described by features related to the various generated representations. We show that a Selective Naive Bayes classifier on this new data set is highly competitive when compared with state-of-the-art times series classification methods while highlighting interpretable and class relevant patterns.
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Notes
- 1.
In contrast to lazy learning, eager learning has an explicit training phase and generally deploys faster.
- 2.
khc and snb are both available at http://www.khiops.com.
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Acknowledgments
We wish to thank Anthony Bagnall and his team from University of East-Anglia for providing tsc-ensemble prototype, Eamonn Keogh and his team from University of California Riverside for providing prototypes of dtw-nn and fast-shapelets.
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Gay, D., Guigourès, R., Boullé, M., Clérot, F. (2014). Feature Extraction over Multiple Representations for Time Series Classification. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2013. Lecture Notes in Computer Science(), vol 8399. Springer, Cham. https://doi.org/10.1007/978-3-319-08407-7_2
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