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Eamonn J. Keogh

Active 1997–2025

285
Papers
39,906
Citations
88
h-index
202
i10-index

Citations

Citations per year for Eamonn J. Keogh1963: 1 citations1970: 1 citations1976: 2 citations1983: 3 citations1987: 1 citations1989: 1 citations1990: 1 citations1997: 2 citations1998: 9 citations1999: 21 citations2000: 43 citations2001: 46 citations2002: 88 citations2003: 129 citations2004: 290 citations2005: 402 citations2006: 493 citations2007: 519 citations2008: 628 citations2009: 691 citations2010: 908 citations2011: 827 citations2012: 1,149 citations2013: 1,210 citations2014: 1,384 citations2015: 1,617 citations2016: 1,598 citations2017: 1,787 citations2018: 2,066 citations2019: 2,226 citations2020: 2,287 citations2021: 1,932 citations2022: 1,799 citations2023: 1,550 citations2024: 1,480 citations2025: 1,196 citations2026: 324 citations2027: 2 citations1964–1969: no citations, so these years are not shown1971–1975: no citations, so these years are not shown1977–1982: no citations, so these years are not shown1984–1986: no citations, so these years are not shown1988: no citations, so this year is not shown1991–1996: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 3,358 citing papers, 19.4% of this breakdownChina: 2,932 citing papers, 16.9% of this breakdownGermany: 997 citing papers, 5.7% of this breakdownUnited Kingdom: 784 citing papers, 4.5% of this breakdownFrance: 774 citing papers, 4.5% of this breakdownAustralia: 720 citing papers, 4.2% of this breakdownItaly: 531 citing papers, 3.1% of this breakdownCanada: 512 citing papers, 3% of this breakdownJapan: 509 citing papers, 2.9% of this breakdownSpain: 491 citing papers, 2.8% of this breakdownIndia: 379 citing papers, 2.2% of this breakdownSouth Korea: 352 citing papers, 2% of this breakdown
0%19.4%Other 28.8%

Institutions

Fields

  • Computer Science76.6%
  • Engineering8.6%
  • Medicine2.5%
  • Biochemistry, Genetics and Molecular Biology1.6%
  • Decision Sciences1.6%
  • Neuroscience1.4%
  • Other7.7%

Topics

  • Time Series Analysis and Forecasting17.9%
  • Anomaly Detection Techniques and Applications8.6%
  • Data Management and Algorithms5%
  • Music and Audio Processing4%
  • Complex Systems and Time Series Analysis3%
  • Data Mining Algorithms and Applications1.9%
  • Other59.6%

Coauthors

All papers

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  1. The UEA multivariate time series classification archive, 2018

    Authors: Anthony J. Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, Eamonn J. Keogh - arXiv (Cornell University), CoRR 2018 cited by 356

  2. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

    Authors: Anthony J. Bagnall, Jason Lines, Aaron Bostrom, James Large, Eamonn J. Keogh - Data Mining and Knowledge Discovery, Data Min. Knowl. Discov. 2016 cited by 1,364

  3. Experiencing SAX: a novel symbolic representation of time series

    Authors: Jessica Lin, Eamonn J. Keogh, Li Wei, Stefano Lonardi - Data Mining and Knowledge Discovery, Data Min. Knowl. Discov. 2007 cited by 1,607

  4. 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, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, Eamonn J. Keogh - IEEE 16th International Conference on Data Mining (ICDM) 2016 cited by 621

  5. Exact indexing of dynamic time warping

    Authors: Eamonn J. Keogh, Chotirat (Ann) Ratanamahatana - Knowledge and Information Systems, Knowl. Inf. Syst. 2004 cited by 1,949

  6. Time series shapelets: a new primitive for data mining

    Authors: Lexiang Ye, Eamonn J. Keogh - SIGKDD international conference on Knowledge discovery and data mining 2009 cited by 963

  7. Searching and mining trillions of time series subsequences under dynamic time warping

    Authors: Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Gustavo E. A. P. A. Batista, M. Brandon Westover, Qiang Zhu, Jesin Zakaria, Eamonn J. Keogh - SIGKDD international conference on Knowledge discovery and data mining 2012 cited by 1,036

  8. Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress

    Authors: Renjie Wu, Eamonn J. Keogh - IEEE Transactions on Knowledge and Data Engineering, IEEE Trans. Knowl. Data Eng. 2021 cited by 140

  9. A symbolic representation of time series, with implications for streaming algorithms

    Authors: Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu - SIGMOD workshop on Research issues in data mining and knowledge discovery, DMKD 2003 cited by 1,788

  10. Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases

    Authors: Eamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra - Knowledge and Information Systems, Knowl. Inf. Syst. 2001 cited by 1,572

  11. Generalizing DTW to the multi-dimensional case requires an adaptive approach

    Authors: Mohammad Shokoohi-Yekta, Bing Hu, Hongxia Jin, Jun Wang, Eamonn J. Keogh - Data Mining and Knowledge Discovery, Data Min. Knowl. Discov. 2016 cited by 323

  12. Querying and mining of time series data: experimental comparison of representations and distance measures

    Authors: Hui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, Eamonn J. Keogh - VLDB Endowment, Proc. VLDB Endow. 2008 cited by 1,347

  13. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification

    Authors: Lexiang Ye, Eamonn J. Keogh - Data Mining and Knowledge Discovery, Data Min. Knowl. Discov. 2010 cited by 387

  14. Experimental comparison of representation methods and distance measures for time series data

    Authors: Xiaoyue Wang, Abdullah Mueen, Hui Ding, Goce Trajcevski, Peter Scheuermann, Eamonn J. Keogh - Data Mining and Knowledge Discovery, Data Min. Knowl. Discov. 2012 cited by 832

  15. MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives

    Authors: Takaaki Nakamura, Makoto Imamura, Ryan Mercer, Eamonn J. Keogh - IEEE International Conference on Data Mining (ICDM) 2020 cited by 100

  16. Derivative Dynamic Time Warping

    Authors: Eamonn J. Keogh, Michael J. Pazzani - SIAM International Conference on Data Mining, SDM 2001 cited by 1,126

  17. The UCR Time Series Archive

    Authors: Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn J. Keogh - IEEE/CAA Journal of Automatica Sinica, IEEE CAA J. Autom. Sinica 2019 cited by 101

  18. CID: an efficient complexity-invariant distance for time series

    Authors: Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Oben Moses Tataw, Vinícius M. A. de Souza - Data Mining and Knowledge Discovery, Data Min. Knowl. Discov. 2013 cited by 350

  19. 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 J. Funning, Abdullah Mueen, Philip Brisk, Eamonn J. Keogh - IEEE 16th International Conference on Data Mining (ICDM) 2016 cited by 229

  20. Generating Synthetic Time Series to Augment Sparse Datasets

    Authors: Germain Forestier, François Petitjean, Hoang Anh Dau, Geoffrey I. Webb, Eamonn J. Keogh - IEEE International Conference on Data Mining (ICDM) 2017 cited by 179

  21. An Online Algorithm for Segmenting Time Series

    Authors: Eamonn J. Keogh, Selina Chu, David M. Hart, Michael J. Pazzani - Proceedings 2001 IEEE International Conference on Data Mining, ICDM 2001 cited by 1,109

  22. Scaling up dynamic time warping for datamining applications

    Authors: Eamonn J. Keogh, Michael J. Pazzani - sixth ACM SIGKDD international conference on Knowledge discovery and data mining 2000 cited by 822

  23. Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds

    Authors: Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Kaveh Kamgar, Eamonn J. Keogh - IEEE International Conference on Data Mining (ICDM) 2018 cited by 107

  24. Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams

    Authors: Yue Lu, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn J. Keogh - SIGKDD Conference on Knowledge Discovery and Data Mining 2022 cited by 56