{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:56:17Z","timestamp":1776884177066,"version":"3.51.2"},"reference-count":23,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:p>Unsupervised (a.k.a. Self-supervised) representation learning (URL) has emerged as a new paradigm for time series analysis, because it has the ability to learn generalizable time series representation beneficial for many downstream tasks without using labels that are usually difficult to obtain. Considering that existing approaches have limitations in the design of the representation encoder and the learning objective, we have proposed Contrastive Shapelet Learning (CSL), the first URL method that learns the general-purpose shapelet-based representation through unsupervised contrastive learning, and shown its superior performance in several analysis tasks, such as time series classification, clustering, and anomaly detection. In this paper, we develop TimeCSL, an end-to-end system that makes full use of the general and interpretable shapelets learned by CSL to achieve explorable time series analysis in a unified pipeline. We introduce the system components and demonstrate how users interact with TimeCSL to solve different analysis tasks in the unified pipeline, and gain insight into their time series by exploring the learned shapelets and representation.<\/jats:p>","DOI":"10.14778\/3685800.3685907","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T17:25:21Z","timestamp":1731086721000},"page":"4489-4492","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis"],"prefix":"10.14778","volume":"17","author":[{"given":"Zhiyu","family":"Liang","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Liang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Liang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Zheng","sequence":"additional","affiliation":[{"name":"CnosDB Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn J. Keogh.","author":"Bagnall Anthony J.","year":"2018","unstructured":"Anthony J. Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn J. Keogh. 2018. The UEA multivariate time series classification archive, 2018. CoRR abs\/1811.00075 (2018)."},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Stefanos Bennett Mihai Cucuringu and Gesine Reinert. 2022. Detection and clustering of lead-lag networks for multivariate time series with an application to financial markets. (2022).","DOI":"10.1007\/s10994-022-06250-4"},{"key":"e_1_2_1_3_1","volume-title":"Xiaoli Li, and Cuntai Guan.","author":"Eldele Emadeldeen","year":"2021","unstructured":"Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan. 2021. Time-Series Representation Learning via Temporal and Contextual Contrasting. In IJCAI. 2352--2359."},{"key":"e_1_2_1_4_1","volume-title":"Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32","author":"Franceschi Jean-Yves","year":"2019","unstructured":"Jean-Yves Franceschi, Aymeric Dieuleveut, and Martin Jaggi. 2019. Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_2_1_5_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2019.2917521"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Zhiyu Liang Chen Liang Zheng Liang Hongzhi Wang and Bo Zheng. 2024. UniTS: A Universal Time Series Analysis Framework Powered by Self-Supervised Representation Learning. In SIGMOD. 480--483.","DOI":"10.1145\/3626246.3654733"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.03.063"},{"key":"e_1_2_1_9_1","volume-title":"FedST: secure federated shapelet transformation for time series classification. The VLDB Journal (July","author":"Liang Zhiyu","year":"2024","unstructured":"Zhiyu Liang and Hongzhi Wang. 2024. FedST: secure federated shapelet transformation for time series classification. The VLDB Journal (July 2024)."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3632093.3632103"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.22381\/ajmr8220214"},{"key":"e_1_2_1_12_1","volume-title":"Learning representations by back-propagating errors. nature 323, 6088","author":"Rumelhart David E","year":"1986","unstructured":"David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1986. Learning representations by back-propagating errors. nature 323, 6088 (1986), 533--536."},{"key":"e_1_2_1_13_1","volume-title":"EEG signal processing","author":"Sanei Saeid","unstructured":"Saeid Sanei and Jonathon A Chambers. 2013. EEG signal processing. John Wiley & Sons."},{"key":"e_1_2_1_14_1","unstructured":"Wensi Tang Guodong Long Lu Liu Tianyi Zhou Michael Blumenstein and Jing Jiang. 2022. Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification. In ICLR. OpenReview.net."},{"key":"e_1_2_1_15_1","unstructured":"Sana Tonekaboni Danny Eytan and Anna Goldenberg. 2021. Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. In ICLR. OpenReview.net."},{"key":"e_1_2_1_16_1","volume-title":"WaveNet: A Generative Model for Raw Audio. CoRR abs\/1609.03499","author":"van den Oord A\u00e4ron","year":"2016","unstructured":"A\u00e4ron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew W. Senior, and Koray Kavukcuoglu. 2016. WaveNet: A Generative Model for Raw Audio. CoRR abs\/1609.03499 (2016)."},{"key":"e_1_2_1_17_1","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research 9, 86 (2008), 2579--2605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_18_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems. 5998--6008."},{"key":"e_1_2_1_19_1","volume-title":"Learning Evolvable Time-series Shapelets","author":"Yamaguchi Akihiro","unstructured":"Akihiro Yamaguchi, Ken Ueo, and Hisashi Kashima. 2022. Learning Evolvable Time-series Shapelets. In ICDE. IEEE, 793--805."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"George Zerveas Srideepika Jayaraman Dhaval Patel Anuradha Bhamidipaty and Carsten Eickhoff. 2021. A transformer-based framework for multivariate time series representation learning. In SIGKDD. 2114--2124.","DOI":"10.1145\/3447548.3467401"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2847699"},{"key":"e_1_2_1_23_1","first-page":"27268","article-title":"FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting","volume":"162","author":"Zhou Tian","year":"2022","unstructured":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. In ICML, Vol. 162. 27268--27286.","journal-title":"ICML"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3685800.3685907","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:25:40Z","timestamp":1735622740000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3685800.3685907"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":23,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["10.14778\/3685800.3685907"],"URL":"https:\/\/doi.org\/10.14778\/3685800.3685907","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"2024-11-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}