{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T21:40:16Z","timestamp":1783633216338,"version":"3.55.0"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2016,7,20]],"date-time":"2016-07-20T00:00:00Z","timestamp":1468972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Seventh Framework Programme of the European Commission, through the REDUCTION","award":["#288254"],"award-info":[{"award-number":["#288254"]}]},{"name":"Deutsche Forschungsgemeinschaft within the project HyLAP"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2017,2,28]]},"abstract":"<jats:p>Motifs are the most repetitive\/frequent patterns of a time-series. The discovery of motifs is crucial for practitioners in order to understand and interpret the phenomena occurring in sequential data. Currently, motifs are searched among series sub-sequences, aiming at selecting the most frequently occurring ones. Search-based methods, which try out series sub-sequence as motif candidates, are currently believed to be the best methods in finding the most frequent patterns.<\/jats:p>\n          <jats:p>\n            However, this paper proposes an entirely new perspective in finding motifs. We demonstrate that searching is non-optimal since the domain of motifs is restricted, and instead we propose a principled optimization approach able to find optimal motifs. We treat the occurrence frequency as a function and time-series motifs as its parameters, therefore we\n            <jats:italic>learn<\/jats:italic>\n            the optimal motifs that maximize the frequency function. In contrast to searching, our method is able to discover the most repetitive patterns (hence optimal), even in cases where they do not explicitly occur as sub-sequences. Experiments on several real-life time-series datasets show that the motifs found by our method are highly more frequent than the ones found through searching, for exactly the same distance threshold.\n          <\/jats:p>","DOI":"10.1145\/2940329","type":"journal-article","created":{"date-parts":[[2016,7,21]],"date-time":"2016-07-21T15:13:24Z","timestamp":1469114004000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Latent Time-Series Motifs"],"prefix":"10.1145","volume":"11","author":[{"given":"Josif","family":"Grabocka","sequence":"first","affiliation":[{"name":"ISMLL, University of Hildesheim, Hildesheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Schilling","sequence":"additional","affiliation":[{"name":"ISMLL, University of Hildesheim, Hildesheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars","family":"Schmidt-Thieme","sequence":"additional","affiliation":[{"name":"ISMLL, University of Hildesheim, Hildesheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,7,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1211447110"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/369133.369172"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the SIAM International Conference on Data Mining (SDM\u201910)","author":"Castro N."},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the SIAM International Conference on Data Mining (SDM\u201911)","author":"Castro N."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/sam.11134"},{"key":"e_1_2_1_6_1","series-title":"Lecture Notes in Computer Science","volume-title":"Proceedings of the Knowledge Discovery in Databases (PKDD\u201906), Johannes Frnkranz, Tobias Scheffer, and Myra Spiliopoulou (Eds.)","author":"Catalano Joe"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956808"},{"key":"e_1_2_1_8_1","unstructured":"Thomas H. Cormen Clifford Stein Ronald L. Rivest and Charles E. Leiserson. 2001. Introduction to Algorithms (2nd ed.). McGraw-Hill Higher Education.   Thomas H. Cormen Clifford Stein Ronald L. Rivest and Charles E. Leiserson. 2001. Introduction to Algorithms (2nd ed.). McGraw-Hill Higher Education."},{"key":"e_1_2_1_9_1","unstructured":"John Duchi Elad Hazan and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12 (July 2011) 2121--2159.   John Duchi Elad Hazan and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12 (July 2011) 2121--2159."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/11893318_12"},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"A. L. Goldberger L. A. N. Amaral L. Glass J. M. Hausdorff P. Ch. Ivanov R. G. Mark J. E. Mietus G. B. Moody C.-K. Peng and H. E. Stanley. 2000 (June 13). PhysioBank PhysioToolkit and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101 23 (2000 (June 13)) e215--e220.  A. L. Goldberger L. A. N. Amaral L. Glass J. M. Hausdorff P. Ch. Ivanov R. G. Mark J. E. Mietus G. B. Moody C.-K. Peng and H. E. Stanley. 2000 (June 13). PhysioBank PhysioToolkit and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. 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