Abstract
Time series classification is important due to its pervasive applications, especially for the emerging Smart City applications that are driven by intelligent sensors. Shapelets are sub-sequences of time series that have highly predictive abilities, and time series represented by shapelets can better reveal the patterns thus have better classification accuracy. Finding shapelets is challenging as its computational in-feasibility, most existing methods only finds shapelets with a same length or a few fixed length shapelets because the searching space of shapelets with arbitrary length is too large. In this paper, we improve the time series classification accuracy by discovering shapelets with arbitrary lengths. We borrow the idea of Apriori algorithm in association rule learning, that is, the superset shapelet candidates of a poor predictive shapelet candidate also have poor predictive abilities. Therefore, we propose a Flexible Shapelets Discovery (FSD) algorithm to discover shapelets with varying lengths. In FSD, shapelet candidates having the lower bound of length are discovered, and then we extend them into arbitrary lengths shapelets as long as their predictive abilities increases. Experiments conducted on 6 UCR time series datasets demonstrate that the arbitrary length shapelets discovered by FSD achieves better classification accuracy than those using fixed length shapelets.
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References
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)
Chen, Y., et al.: The UCR time series classification archive, July 2015. www.cs.ucr.edu/~eamonn/time_series_data/
Grabocka, J., Schilling, N., Wistuba, M., Schmidt-Thieme, L.: Learning time-series shapelets. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 392–401. ACM (2014)
Grabocka, J., Wistuba, M., Schmidt-Thieme, L.: Fast classification of univariate and multivariate time series through shapelet discovery. Knowl. Inf. Syst. 49(2), 429–454 (2016)
Hou, L., Kwok, J.T., Zurada, J.M.: Efficient learning of timeseries shapelets. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Kruskal, J.B.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1), 1–27 (1964)
Li, F., He, J., Huang, G., Zhang, Y., Shi, Y., Zhou, R.: Node-coupling clustering approaches for link prediction. Knowl. Based Syst. 89, 669–680 (2015)
Piyare, R.: Internet of things: ubiquitous home control and monitoring system using android based smart phone. Int. J. Internet Things 2(1), 5–11 (2013)
Tang, L., Yu, L., Liu, F., Xu, W.: An integrated data characteristic testing scheme for complex time series data exploration. Int. J. Inf. Technol. Decis. Mak. 12(03), 491–521 (2013)
Tsaur, R.C., Wang, H.F., Yang, J.C.: Fuzzy regression for seasonal time series analysis. Int. J. Inf. Technol. Decis. Mak. 1(01), 165–175 (2002)
Ulanova, L., Begum, N., Keogh, E.: Scalable clustering of time series with U-shapelets. In: Proceedings of the 2015 SIAM International Conference on Data Mining, SIAM, pp. 900–908 (2015)
Wistuba, M., Grabocka, J., Schmidt-Thieme, L.: Ultra-fast shapelets for time series classification. arXiv preprint arXiv:1503.05018 (2015)
Xu, Y., Zeng, X., Koehl, L.: An intelligent sensory evaluation method for industrial products characterization. Int. J. Inf. Technol. Decis. Mak. 6(02), 349–370 (2007)
Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956. ACM (2009)
Zakaria, J., Mueen, A., Keogh, E.: Clustering time series using unsupervised-shapelets. In: 2012 IEEE 12th International Conference on Data Mining, pp. 785–794. IEEE (2012)
Zhang, Q., Wu, J., Yang, H., Tian, Y., Zhang, C.: Unsupervised feature learning from time series. In: IJCAI, pp. 2322–2328 (2016)
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Cai, B., Huang, G., Turkedjieva, M.A., Xiang, Y., Chi, CH. (2020). Flexible Shapelets Discovery for Time Series Classification. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_21
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DOI: https://doi.org/10.1007/978-981-15-2810-1_21
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