Abstract
Dynamic Time Warping (DTW) is one of the best similarity measures for time series, and it has extensively been used in retrieval, classification or mining applications. It is a costly measure, and applying it to numerous and/or very long times series is difficult in practice. Recently, Shapelet Transform (ST) proved to enable accurate supervised classification of time series. ST learns small subsequences that well discriminate classes, and transforms the time series into vectors lying in a metric space. In this paper, we adopt the ST framework in a novel way: we focus on learning, without class label information, shapelets such that Euclidean distances in the ST-space approximate well the true DTW. Our approach leads to an ubiquitous representation of time series in a metric space, where any machine learning method (supervised or unsupervised) and indexing system can operate efficiently.
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References
Bagnall, A., Lines, J., Vickers, W., Keogh, E.: The UEA and UCR time series classification repository. www.timeseriesclassification.com
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a “siamese” time delay neural network. In: Advances in Neural Information Processing Systems, pp. 737–744 (1994)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. JMLR 12, 2121–2159 (2011)
Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45(1) (2012)
Grabocka, J., Schilling, N., Wistuba, M., Schmidt-Thieme, L.: Learning time-series shapelets. In: Proceedings of KDD (2014)
Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. DMKD 28(4), 851–881 (2014)
Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. KAIS 7, 358–386 (2005)
Lei, Q., Yi, J., Vaculín, R., Wu, L., Dhillon, I.S.: Similarity preserving representation learning for time series analysis (2017). http://arxiv.org/abs/1702.03584
Lemire, D.: Faster retrieval with a two-pass dynamic-time-warping lower bound. Pattern Recogn. 42(9), 2169–2180 (2009)
Paparrizos, J., Gravano, L.: k-Shape: efficient and accurate clustering of time series. In: Proceedings of SIGMOD (2015)
Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., Keogh, E.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of KDD (2012)
Tan, C.W., Webb, G.I., Petitjean, F.: Indexing and classifying gigabytes of time series under time warping. In: Proceedings of SIAM ICDM (2017)
Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of KDD (2009)
Zakaria, J., Mueen, A., Keogh, E.: Clustering time series using unsupervised-shapelets. In: Proceedings of ICDM (2012)
Zhang, Q., Wu, J., Yang, H., Tian, Y., Zhang, C.: Unsupervised feature learning from time series. In: Proceedings of IJCAI (2016)
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Lods, A., Malinowski, S., Tavenard, R., Amsaleg, L. (2017). Learning DTW-Preserving Shapelets. In: Adams, N., Tucker, A., Weston, D. (eds) Advances in Intelligent Data Analysis XVI. IDA 2017. Lecture Notes in Computer Science(), vol 10584. Springer, Cham. https://doi.org/10.1007/978-3-319-68765-0_17
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DOI: https://doi.org/10.1007/978-3-319-68765-0_17
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