{"type":"ScholarlyArticle","title":"Time Series Chains: A Novel Tool for Time Series Data Mining","authors":[{"name":"Yan Zhu","slug":"yan-zhu","given":"Yan","family":"Zhu","affiliation":"University of California , Riverside"},{"name":"Makoto Imamura","slug":"makoto-imamura","given":"Makoto","family":"Imamura","affiliation":"Tokai University"},{"name":"Daniel Nikovski","slug":"daniel-nikovski","given":"Daniel","family":"Nikovski","affiliation":"Mitsubishi Electric Research Laboratories"},{"name":"Eamonn Keogh","slug":"eamonn-keogh","given":"Eamonn","family":"Keogh","affiliation":"University of California , Riverside"}],"abstract":"Since their introduction over a decade ago, time se-ries motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns are arbi-trarily dissimilar. In the discrete space, this is simi-lar to extracting the text chain “hit, hot, dot, dog” from a paragraph. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time Series Chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce a robust definition of time series chains, and a scalable algorithm that allows us to discover them in massive datasets.","doi":"10.24963/ijcai.2018/764","url":"https://ijcai.org/proceedings/2018/764","pdf":"https://ijcai.org/proceedings/2018/0764.pdf","markdown":"https://ijcai.org/proceedings/2018/764.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/764","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Sister Conferences Best Papers","track":"Best Sister Conferences","number":764,"pages":{"first":5414,"last":5418},"keywords":["Machine Learning: Time-series;Data Streams","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Hao et al., 2012] Ming C. Hao, Manish Marwah, Halldór Janetzko, Umeshwar Dayal, Daniel A. Keim, Debprakash Patnaik, Naren Ramakrishnan and Ratnesh K. Sharma, Visual exploration of frequent patterns in multivariate time series. Information Visualization, 11.1(2012): 71-83.","title":"Visual exploration of frequent patterns in multivariate time series","authors":"Ming C Hao, Manish Marwah, Halldór Janetzko, Umeshwar Dayal, Daniel A Keim, Debprakash Patnaik, Naren Ramakrishnan, Ratnesh K Sharma","year":"2012","venue":"Information Visualization","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Heldt et al., 2003] T. Heldt, M. B. Oefinger, M. Hoshiyama, and R. G. Mark, Circulatory response to passive and ac- tive changes in posture. In Computers in Cardiology (2003): 263-266. IEEE.","title":"Circulatory response to passive and active changes in posture","authors":"T Heldt, M B Oefinger, M Hoshiyama, R G Mark","year":"2003","venue":"Computers in Cardiology","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Hoang et al., 2015] Thang Hoang, Deokjai Choi, and Thuc Nguyen, On the instability of sensor orientation in gait verification on mobile phone. In 12th IEEE International Joint Conference on e-Business and Telecommunications (ICETE), 4(2015): 148-159.","title":"On the instability of sensor orientation in gait verification on mobile phone","authors":"Thang Hoang, Deokjai Choi, Thuc Nguyen","year":"2015","venue":"12th IEEE International Joint Conference on e-Business and Telecommunications (ICETE)","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Lovallo et al., 2004] William R. Lovallo, Michael F. Wil- son, Andrea S. Vincent, Bong Hee Sung, Barbara S. McKey, and Thomas L. Whitsett, Blood pressure re- sponse to caffeine shows incomplete tolerance after short-term regular consumption. Hypertension, 43.4 (2004): 760-765.","title":"Blood pressure response to caffeine shows incomplete tolerance after short-term regular consumption","authors":"William R Lovallo, Michael F Wilson, Andrea S Vincent, Bong Hee Sung, Barbara S Mckey, Thomas L Whitsett","year":"2004","venue":"Hypertension","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Matsubara et al., 2015] Yasuko Matsubara, Yasushi Sakurai, and Christos Faloutsos, The web as a jungle: Non-linear dynamical systems for co-evolving online activities. In Proceedings of the 24th International Conference on World Wide Web, 2015, pp. 721-731.","title":"The web as a jungle: Non-linear dynamical systems for co-evolving online activities","authors":"Yasuko Matsubara, Yasushi Sakurai, Christos Faloutsos","year":"2015","venue":"Proceedings of the 24th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Moya, 2009] Angel Moya, Tilt testing and neurally medi- ated syncope: too many protocols for one condition or specific protocols for different situations?. Eur Heart J. 30.18 (2009): 2174-2176.","title":"Angel Moya, Tilt testing and neurally mediated syncope: too many protocols for one condition or specific protocols for different situations?","authors":"Moya","year":"2009","venue":"Eur Heart J","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Murray et al., 2015] D. Murray, J. Liao, L. Stankovic, V. Stankovic, R. Hauxwell-Baldwin, C. Wilson, M. Cole- man, T. Kane, S. Firth, A data management platform for personalised real-time energy feedback. EEDAL, 2015.","title":"A data management platform for personalised real-time energy feedback","authors":"D Murray, J Liao, L Stankovic, V Stankovic, R Hauxwell-Baldwin, C Wilson, M Coleman, T Kane, S Firth","year":"2015","venue":"EEDAL","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Patel et al., 2002] Pranav Patel, Eamonn Keogh, Jessica Lin, and Stefano Lonardi, Mining motifs in massive time se- ries databases. In Proceedings of the 2002 IEEE Interna- tional Conference on Data Mining. pp 370-377.","title":"Mining motifs in massive time series databases","authors":"Pranav Patel, Eamonn Keogh, Jessica Lin, Stefano Lonardi","year":null,"venue":"Proceedings of the 2002 IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ponganis et al., 2015] P.J. Ponganis, J. St Leger and M. Scadeng, Penguin lungs and air sacs: implications for baroprotection, oxygen stores and buoyancy. Journal of Experimental Biology. (2015): 720-730.","title":"Penguin lungs and air sacs: implications for baroprotection, oxygen stores and buoyancy","authors":"P J Ponganis, J St Leger, M Scadeng","year":"2015","venue":"Journal of Experimental Biology","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Shokoohi-Yekta et al., 2015] Mohammad Shokoohi-Yekta, Yanping Chen, Bilson Campana, Bing Hu, Jesin Zakaria, and Eamonn Keogh, Discovery of meaningful rules in time series. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 2015, pp. 1085-1094.","title":"Discovery of meaningful rules in time series","authors":"Mohammad Shokoohi-Yekta, Yanping Chen, Bilson Campana, Bing Hu, Jesin Zakaria, Eamonn Keogh","year":"2015","venue":"Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Smith, 2010] Jordan Smith, The Accidentally-on-Purpose History of Cyber Monday. 2010. URL retrieved February 5 th 2017: www.esquire.com/news-politics/news/a23870/cyber-mo nday-online-shopping-4021548/","title":"Jordan Smith, The Accidentally-on-Purpose History of Cyber Monday","authors":"Smith","year":"2010","venue":null,"doi":null,"url":"www.esquire.com/news-politics/news/a23870/cyber-monday-online-shopping-4021548/","internal_url":null},{"position":12,"raw":"[Syed et al., 2010] Zeeshan Syed, Collin Stultz, Manolis Kellis, Piotr Indyk, and John Guttag, Motif discovery in physiological datasets: a methodology for inferring pre- dictive elements. ACM Transactions on Knowledge Discovery from Data (TKDD), 4.1(2010): 2.","title":"Motif discovery in physiological datasets: a methodology for inferring predictive elements","authors":"Zeeshan Syed, Collin Stultz, Manolis Kellis, Piotr Indyk, John Guttag","year":"2010","venue":"ACM Transactions on Knowledge Discovery from Data (TKDD)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Williams et al., 2011] Cassondra L. Williams, Katsufumi Sato, Kozue Shiomi, and Paul J. Ponganis, Muscle energy stores and stroke rates of emperor penguins: implications for muscle metabolism and dive performance. Physio- logical and Biochemical Zoology 85.2(2011): 120-133.","title":"Muscle energy stores and stroke rates of emperor penguins: implications for muscle metabolism and dive performance","authors":"Cassondra L Williams, Katsufumi Sato, Kozue Shiomi, Paul J Ponganis","year":"2011","venue":"Physiological and Biochemical Zoology","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Yeh et al., 2016] Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh, Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View that Includes Motifs, Discords and Shapelets. In Proceedings of the 2016 IEEE 16th International Conference on Data Min- ing (ICDM), pp. 1317-1322.","title":"Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View that Includes Motifs, Discords and Shapelets","authors":"Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Anh Hoang, Diego Furtado Dau, Abdullah Silva, Eamonn Mueen, Keogh","year":null,"venue":"Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Zhu et al., 2016] Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah Mueen, Philip Brisk, and Eamonn Keogh, Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 739-748.","title":"Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins","authors":"Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah Mueen, Philip Brisk, Eamonn Keogh","year":null,"venue":"Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Supporting Webpage, 2017] Supporting Webpage: https://sites.google.com/site/timeserieschain/","title":"Supporting Webpage","authors":null,"year":"2017","venue":null,"doi":null,"url":"https://sites.google.com/site/timeserieschain/","internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}