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Invariant time-series factorization

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Abstract

Time-series analysis is an important domain of machine learning and a plethora of methods have been developed for the task. This paper proposes a new representation of time series, which in contrast to existing approaches, decomposes a time-series dataset into latent patterns and membership weights of local segments to those patterns. The process is formalized as a constrained objective function and a tailored stochastic coordinate descent optimization is applied. The time-series are projected to a new feature representation consisting of the sums of the membership weights, which captures frequencies of local patterns. Features from various sliding window sizes are concatenated in order to encapsulate the interaction of patterns from different sizes. The derived representation offers a set of features that boosts classification accuracy. Finally, a large-scale experimental comparison against 11 baselines over 43 real life datasets, indicates that the proposed method achieves state-of-the-art prediction accuracy results.

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Notes

  1. www.cs.ucr.edu/~eamonn/time_series_data.

  2. http://fs.ismll.de/publicspace/InvariantFactorization/.

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Acknowledgments

Partially co-funded by the Seventh Framework Programme of the European Comission, through project REDUCTION (# 288254). www.reduction-project.eu.

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Authors and Affiliations

  1. Information Systems and Machine Learning Lab, Samelsonplatz 22, 31141,  Hildesheim, Germany

    Josif Grabocka & Lars Schmidt-Thieme

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  1. Josif Grabocka
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  2. Lars Schmidt-Thieme
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Correspondence to Josif Grabocka.

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Responsible editors: Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo.

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Grabocka, J., Schmidt-Thieme, L. Invariant time-series factorization. Data Min Knowl Disc 28, 1455–1479 (2014). https://doi.org/10.1007/s10618-014-0364-z

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  • DOI: https://doi.org/10.1007/s10618-014-0364-z

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