Abstract
Time series classification (TSC) is a hot topic in data mining field in the past decade. Among them, classifier based on shapelet has the advantage of interpretability, high accuracy and high speed. Shapelet is a discriminative sub-sequence of time series, which can maximally represent a class. Traditional fast shapelet algorithm uses SAX to represent time series. However, SAX usually loses the trend information of the series. In order to solve the problem, a trend-based fast shapelet discovery algorithm has been proposed. Firstly, the method of trend feature symbolization is used to represent time series. Then, a random mask is applied to select the candidate shapelets. Finally, the best shapelet is selected. The experimental results show that our algorithm is very competitive.
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References
Yan, W., Li, G.: Research on time series classification based on shapelet. Comput. Sci. 046(001), 29–35 (2019)
Zhao, C., Wang, T., Liu, S., et al.: A fast time series shapelet discovery algorithm combining selective ex-traction and subclass clustering. J. Softw. 000(003), 763–777 (2020)
Zhang, Z., Zhang, H., Wen, Y., Yuan, X.: Accelerating time series shapelets discovery with key points. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds.) APWeb 2016. LNCS, vol. 9932, pp. 330–342. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45817-5_26
Ye, L., Keogh, E.: Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Mining Knowl. Discovery 22(1–2), 149–182 (2011)
Bagnall, A., Lines, J., Bostrom, A., et al.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining Knowl. Discov. 31(3) (2017)
Rakthanmanon, T., Keogh, E.: Fast shapelets: a scalable algorithm for discovering time series shapelets. In: Proceedings of the 2013 SIAM International Conference on Data Mining. Philadelphia: Society for Industrial and Applied Mathematics, pp. 668–676 (2013)
Wei,Y., Jiao, L., Wang, S., et al.: Time series classification with max-correlation and min-redundancy shapelets transformation. In: International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI), Beijing, China, pp. 7–12 (2015)
Renard, X., Rifqi, M., Detyniecki, M.: Random-shapelet: an algorithm for fast shapelet discovery. In: IEEE International Conference on Data Science and Advanced Analytics, pp. 1–10. IEEE (2015)
Grabocka, J., Wistuba, M., Schmidt-Thieme, L.: Fast classification of univariate and multivariate time series through shapelet discovery. Knowl. Inf. Syst. 49(2), 1–26 (2015)
Lin, J., Keogh, E., Li, W., et al.: Experiencing SAX: a novel symbolic representation of time series. Data Mining Knowl. Discov. 15(2), 107–144 (2007)
Zhang, K., Li, Y., Chai, Y., et al.: Trend-based symbolic aggregate approximation for time series representation. In: Chinese Control and Decision Conference (CCDC). Shenyang, pp. 2234–2240 (2018)
Ji, C., Zhao, C., Lei, P., et al.: A fast shapelet discovery algorithm based on important data points. Int. J. Web Serv. Res. 14(2), 67–80 (2017)
Bagnall, A., Lines, J., Keogh, E., et al.: The UEA and UCR time series classification repository (2016). www.timeseriesclassification.com
Ji, C., Zhao, C., Liu, S., et al.: A fast shapelet selection algorithm for time series classification. Comput. Netw. 148, 231–240 (2019)
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Zhang, S., Zheng, X., Ji, C. (2021). Fast Shapelet Discovery with Trend Feature Symbolization. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_26
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DOI: https://doi.org/10.1007/978-981-16-2540-4_26
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