World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
104
Citations
62208
World Ranking
299
National Ranking
164

Overview

Eamonn Keogh is affiliated with the University of California, Riverside in the United States. Their research lies predominantly within the field of Computer Science, with a focus on several related subfields including Signal Processing, Artificial Intelligence, Economics and Econometrics, Computer Vision and Pattern Recognition, and Geophysics.

The main topics covered in the scientist's work highlight a specialization in time-sensitive data analysis and pattern recognition. These topics include:

  • Time Series Analysis and Forecasting
  • Anomaly Detection Techniques and Applications
  • Complex Systems and Time Series Analysis
  • Advanced Text Analysis Techniques
  • Data Visualization and Analytics
  • Music and Audio Processing
  • Data Stream Mining Techniques

Significant publication venues where Eamonn Keogh has contributed include:

  • arXiv (Cornell University)
  • Data Mining and Knowledge Discovery
  • 2022 IEEE 38th International Conference on Data Engineering (ICDE)
  • 2022 IEEE International Conference on Data Mining (ICDM)
  • Institutional Research Information System University of Ferrara (University of Ferrara)

On frequent collaboration, the scientist has worked extensively with several researchers, including:

  • Chin-Chia Michael Yeh
  • Junpeng Wang
  • Zhongfang Zhuang
  • Ryan Mercer
  • Audrey Der

The following are some recent papers authored or co-authored by Eamonn Keogh, illustrating their research interests and areas of expertise:

  • Knowledge extraction with interval temporal logic decision trees (2020), Institutional Research Information System University of Ferrara (University of Ferrara)
  • Matrix profile goes MAD: variable-length motif and discord discovery in data series (2020), Data Mining and Knowledge Discovery
  • Time series motifs discovery under DTW allows more robust discovery of conserved structure (2021), Data Mining and Knowledge Discovery
  • FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates (2020), IEEE Transactions on Knowledge and Data Engineering
  • Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams (2022), Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Best Publications

  • Exact indexing of dynamic time warping

    Eamonn Keogh;Chotirat Ann Ratanamahatana

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

    Jessica Lin;Eamonn Keogh;Stefano Lonardi;Bill Chiu

  • On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration

    Eamonn Keogh;Shruti Kasetty

  • Dimensionality reduction for fast similarity search in large time series databases

    Eamonn J. Keogh;Kaushik Chakrabarti;Michael J. Pazzani;Sharad Mehrotra

  • Experiencing SAX: a novel symbolic representation of time series

    Jessica Lin;Eamonn Keogh;Li Wei;Stefano Lonardi

  • Exact indexing of dynamic time warping

    Eamonn Keogh

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

    Hui Ding;Goce Trajcevski;Peter Scheuermann;Xiaoyue Wang

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

    Anthony Bagnall;Jason Lines;Aaron Bostrom;James Large

  • An online algorithm for segmenting time series

    E. Keogh;S. Chu;D. Hart;M. Pazzani

  • Derivative Dynamic Time Warping.

    Eamonn J. Keogh;Michael J. Pazzani

  • Searching and mining trillions of time series subsequences under dynamic time warping

    Thanawin Rakthanmanon;Bilson Campana;Abdullah Mueen;Gustavo Batista

  • Time series shapelets: a new primitive for data mining

    Lexiang Ye;Eamonn Keogh

  • Locally adaptive dimensionality reduction for indexing large time series databases

    Eamonn Keogh;Kaushik Chakrabarti;Michael Pazzani;Sharad Mehrotra

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

    Xiaoyue Wang;Abdullah Mueen;Hui Ding;Goce Trajcevski

  • Scaling up dynamic time warping for datamining applications

    Eamonn J. Keogh;Michael J. Pazzani

  • HOT SAX: efficiently finding the most unusual time series subsequence

    E. Keogh;J. Lin;A. Fu

  • Segmenting Time Series: A Survey and Novel Approach

    Eamonn Keogh;Selina Chu;David Hart;Michael Pazzani

  • The UCR time series archive

    Hoang Anh Dau;Anthony Bagnall;Kaveh Kamgar;Chin-Chia Michael Yeh

  • Clustering of time-series subsequences is meaningless: implications for previous and future research

    Eamonn Keogh;Jessica Lin

  • Towards parameter-free data mining

    Eamonn Keogh;Stefano Lonardi;Chotirat Ann Ratanamahatana

  • Chapter 36 – Exact Indexing of Dynamic Time Warping

    Eamonn Keogh

  • Experimental Comparison of Representation Methods and Distance Measures for Time Series Data

    Xiaoyue Wang;Hui Ding;Goce Trajcevski;Peter Scheuermann

Frequent Co-Authors

Abdullah Mueen
Abdullah Mueen University of New Mexico
Stefano Lonardi
Stefano Lonardi University of California, Riverside
Gustavo E. A. P. A. Batista
Gustavo E. A. P. A. Batista University of New South Wales
Michael J. Pazzani
Michael J. Pazzani University of California, Riverside
Michail Vlachos
Michail Vlachos University of Lausanne
Dimitrios Gunopulos
Dimitrios Gunopulos National and Kapodistrian University of Athens
Themis Palpanas
Themis Palpanas Université Paris Cité
Ada Wai-Chee Fu
Ada Wai-Chee Fu Chinese University of Hong Kong
Philip Brisk
Philip Brisk University of California, Riverside
Geoffrey I. Webb
Geoffrey I. Webb Monash University

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