Eamonn Keogh

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Abdullah MueenProfessor, Department of Computer Science, University of New MexicoVerified email at cs.unm.edu
Chotirat Ann RatanamahatanaAssociate Professor, Dept. of Computer Engineering, Chulalongkorn UniversityVerified email at chula.ac.th
Jessica LinGeorge Mason UniversityVerified email at gmu.edu
Stefano LonardiProfessor of Computer Science, University of California, RiversideVerified email at cs.ucr.edu
Gustavo Enrique BatistaAssociate Professor, School of Computer Science and Engineering, University of New South WalesVerified email at cse.unsw.edu.au
Yan ZhuPhD Candidate in Computer Science, University of California, RiversideVerified email at ucr.edu
Michael J. PazzaniDistinguished Principal ScientistVerified email at isi.edu
Chin-Chia Michael YehVisa ResearchVerified email at visa.com
Thanawin RakthanmanonDept. of Computer Engineering, Kasetsart University, ThailandVerified email at ku.ac.th
Tony BagnallProfessor of Computer Science, University of SouthamptonVerified email at uea.ac.uk
Bing HuFacebookVerified email at ucr.edu
Yanping ChenUniversity of California, RiversideVerified email at ucr.edu
Michalis VlachosProfessor, HEC Lausanne, University of LausanneVerified email at unil.ch
Qiang ZhuIntuitVerified email at cs.ucr.edu
Nurjahan BegumUniversity of California, RiversideVerified email at cs.ucr.edu
Hoang Anh DauMachine Learning Research Engineer, UberVerified email at uber.com
Zachary ZimmermanGoogleVerified email at google.com
Diego Furtado SilvaAssistant Professor, University of São PauloVerified email at usp.br
Bill ChiuVerified email at tapiama.org
Shaghayegh GharghabiDepartment of Electrical and Computer Engineering, University of California, RiversideVerified email at ucr.edu

Eamonn Keogh

Distinguished Professor of Computer Science, University of California - Riverside

Verified email at cs.ucr.edu - Homepage

Time Series Time Series Mining motif_discovery similarity_search anomaly_detection


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Exact indexing of dynamic time warping E Keogh, CA Ratanamahatana Knowledge and information systems 7, 358-386, 2005	3490	2005
A symbolic representation of time series, with implications for streaming algorithms J Lin, E Keogh, S Lonardi, B Chiu Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining …, 2003	2807	2003
Dimensionality reduction for fast similarity search in large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra Knowledge and information Systems 3, 263-286, 2001	2197	2001
Experiencing SAX: a novel symbolic representation of time series J Lin, E Keogh, L Wei, S Lonardi Data Mining and knowledge discovery 15, 107-144, 2007	2102	2007
On the need for time series data mining benchmarks: a survey and empirical demonstration E Keogh, S Kasetty Proceedings of the eighth ACM SIGKDD international conference on Knowledge …, 2002	1865	2002
Querying and mining of time series data: experimental comparison of representations and distance measures H Ding, G Trajcevski, P Scheuermann, X Wang, E Keogh Proceedings of the VLDB Endowment 1 (2), 1542-1552, 2008	1804	2008
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances A Bagnall, J Lines, A Bostrom, J Large, E Keogh Data mining and knowledge discovery 31, 606-660, 2017	1746	2017
An online algorithm for segmenting time series E Keogh, S Chu, D Hart, M Pazzani Proceedings 2001 IEEE international conference on data mining, 289-296, 2001	1681	2001
Time series shapelets: a new primitive for data mining L Ye, E Keogh Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	1351	2009
Searching and mining trillions of time series subsequences under dynamic time warping T Rakthanmanon, B Campana, A Mueen, G Batista, B Westover, Q Zhu, ... Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012	1304	2012
Locally adaptive dimensionality reduction for indexing large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra Proceedings of the 2001 ACM SIGMOD international conference on Management of …, 2001	1300	2001
Hot sax: Efficiently finding the most unusual time series subsequence E Keogh, J Lin, A Fu Fifth IEEE International Conference on Data Mining (ICDM'05), 8 pp., 2005	1148	2005
Scaling up dynamic time warping for datamining applications EJ Keogh, MJ Pazzani Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	1111	2000
Experimental comparison of representation methods and distance measures for time series data X Wang, A Mueen, H Ding, G Trajcevski, P Scheuermann, E Keogh Data Mining and Knowledge Discovery 26, 275-309, 2013	1102	2013
The UCR time series classification archive Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista July, 2015	1025	2015
Segmenting time series: A survey and novel approach E Keogh, S Chu, D Hart, M Pazzani Data mining in time series databases, 1-21, 2004	936	2004
Towards parameter-free data mining E Keogh, S Lonardi, CA Ratanamahatana Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004	870	2004
Clustering of time-series subsequences is meaningless: implications for previous and future research E Keogh, J Lin Knowledge and information systems 8, 154-177, 2005	868	2005
The UCR time series archive HA Dau, A Bagnall, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... IEEE/CAA Journal of Automatica Sinica 6 (6), 1293-1305, 2019	867	2019
An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. EJ Keogh, MJ Pazzani Kdd 98, 239-243, 1998	844	1998

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