| Exact indexing of dynamic time warping E Keogh, CA Ratanamahatana Knowledge and information systems 7, 358-386, 2005 | 3445 | 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 | 2784 | 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 | 2172 | 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 | 2084 | 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 | 1855 | 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 | 1791 | 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 | 1695 | 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 | 1668 | 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 | 1336 | 2009 |
| 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 | 1280 | 2001 |
| 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 | 1276 | 2012 |
| 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 | 1140 | 2005 |
| Scaling up dynamic time warping for datamining applications EJ Keogh, MJ Pazzani Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000 | 1103 | 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 | 1082 | 2013 |
| The UCR time series classification archive Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista July, 2015 | 1020 | 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 | 952 | 2004 |
| Towards parameter-free data mining E Keogh, S Lonardi, CA Ratanamahatana Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 861 | 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 | 859 | 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 | 827 | 2019 |
| Probabilistic discovery of time series motifs B Chiu, E Keogh, S Lonardi Proceedings of the ninth ACM SIGKDD international conference on Knowledge …, 2003 | 826 | 2003 |
| 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 | 826 | 1998 |
| A brief survey on sequence classification Z Xing, J Pei, E Keogh ACM Sigkdd Explorations Newsletter 12 (1), 40-48, 2010 | 806 | 2010 |
| Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets CCM Yeh, Y Zhu, L Ulanova, N Begum, Y Ding, HA Dau, DF Silva, ... 2016 IEEE 16th international conference on data mining (ICDM), 1317-1322, 2016 | 756 | 2016 |
| Fast time series classification using numerosity reduction X Xi, E Keogh, C Shelton, L Wei, CA Ratanamahatana Proceedings of the 23rd international conference on Machine learning, 1033-1040, 2006 | 712 | 2006 |
| Naïve Bayes. GI Webb, E Keogh, R Miikkulainen Encyclopedia of machine learning 15 (1), 713-714, 2010 | 606 | 2010 |
| Making time-series classification more accurate using learned constraints CA Ratanamahatana, E Keogh Proceedings of the 2004 SIAM international conference on data mining, 11-22, 2004 | 592 | 2004 |
| Finding surprising patterns in a time series database in linear time and space E Keogh, S Lonardi, BY Chiu Proceedings of the eighth ACM SIGKDD international conference on Knowledge …, 2002 | 590 | 2002 |
| Exact discovery of time series motifs A Mueen, E Keogh, Q Zhu, S Cash, B Westover Proceedings of the 2009 SIAM international conference on data mining, 473-484, 2009 | 588 | 2009 |
| Indexing multi-dimensional time-series with support for multiple distance measures M Vlachos, M Hadjieleftheriou, D Gunopulos, E Keogh Proceedings of the ninth ACM SIGKDD international conference on Knowledge …, 2003 | 575 | 2003 |
| Curse of dimensionality. EJ Keogh, A Mueen Encyclopedia of machine learning and data mining 2017, 314-315, 2017 | 573 | 2017 |
| Locally adaptive dimensionality reduction for indexing large time series databases K Chakrabarti, E Keogh, S Mehrotra, M Pazzani ACM Transactions on Database Systems (TODS) 27 (2), 188-228, 2002 | 571 | 2002 |
| Three myths about dynamic time warping data mining CA Ratanamahatana, E Keogh Proceedings of the 2005 SIAM international conference on data mining, 506-510, 2005 | 544 | 2005 |
| Fast shapelets: A scalable algorithm for discovering time series shapelets T Rakthanmanon, E Keogh proceedings of the 2013 SIAM International Conference on Data Mining, 668-676, 2013 | 539 | 2013 |
| The UEA multivariate time series classification archive, 2018 A Bagnall, HA Dau, J Lines, M Flynn, J Large, A Bostrom, P Southam, ... arXiv preprint arXiv:1811.00075, 2018 | 532* | 2018 |
| Time series shapelets: a novel technique that allows accurate, interpretable and fast classification L Ye, E Keogh Data mining and knowledge discovery 22, 149-182, 2011 | 473 | 2011 |
| Everything you know about dynamic time warping is wrong CA Ratanamahatana, E Keogh Third workshop on mining temporal and sequential data 32, 2004 | 473 | 2004 |
| UCI repository of machine learning databases E Keogh, C Blake, CJ Merz Irvine, CA: Uni of California, Department of Information and Computer Science, 1998 | 472* | 1998 |
| Logical-shapelets: an expressive primitive for time series classification A Mueen, E Keogh, N Young Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 440 | 2011 |
| CID: an efficient complexity-invariant distance for time series GE Batista, EJ Keogh, OM Tataw, VMA De Souza Data Mining and Knowledge Discovery 28, 634-669, 2014 | 426 | 2014 |
| Semi-supervised time series classification L Wei, E Keogh Proceedings of the 12th ACM SIGKDD international conference on Knowledge …, 2006 | 424 | 2006 |
Abdullah MueenAssociate Professor, Department of Computer Science, University of New MexicoVerified email at cs.unm.edu
