Unsupervised feature learning from time series

Zhang Qin, Jia Wu, Yang Hong, Yingjie Tian, Zhang Chengqi

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

30 Citations (Scopus)


In this paper we study the problem of learning discriminative features (segments), often referred to as shapelets [Ye and Keogh, 2009] of time series, from unlabeled time series data. Discovering shapelets for time series classification has been widely studied, where many search-based algorithms are proposed to efficiently scan and select segments from a pool of candidates. However, such types of search-based algorithms may incur high time cost when the segment candidate pool is large. Alternatively, a recent work [Grabocka et al., 2014] uses regression learning to directly learn, instead of searching for, shapelets from time series. Motivated by the above observations, we propose a new Unsupervised Shapelet Learning Model (USLM) to efficiently learn shapelets from unlabeled time series data. The corresponding learning function integrates the strengths of pseudo-class label, spectral analysis, shapelets regularization term and regularized least-squares to auto-learn shapelets, pseudo-class labels and classification boundaries simultaneously. A coordinate descent algorithm is used to iteratively solve the learning function. Experiments show that USLM outperforms searchbased algorithms on real-world time series data.

Original languageEnglish
Title of host publicationIJCAI 2016
Subtitle of host publicationProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
EditorsGerhard Brewka
Place of PublicationPalo Alto, CA
PublisherAssociation for the Advancement of Artificial Intelligence
Number of pages7
ISBN (Electronic)9781577357704, 9781577357711
Publication statusPublished - 2016
Externally publishedYes
EventInternational Joint Conferences on Artificial Intelligence (25th : 2016) - New York, United States
Duration: 9 Jul 201615 Jul 2016


ConferenceInternational Joint Conferences on Artificial Intelligence (25th : 2016)
CountryUnited States
CityNew York


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