Eamonn Keogh

44.8k citations

271 papers · 27.0k · 18 hit papers · h-index 77

Impact in

Signal Processing top 0.01%
- Time Series Analysis and Forecasting
- Data Management and Algorithms
- Music and Audio Processing
Artificial Intelligence top 0.01%
- Anomaly Detection Techniques and Applications
- Advanced Text Analysis Techniques
- Data Stream Mining Techniques

Papers in

Signal Processing 218
- Time Series Analysis and Forecasting 210
- Music and Audio Processing 74
- Data Management and Algorithms 73
Artificial Intelligence 136
- Anomaly Detection Techniques and Applications 78
- Advanced Text Analysis Techniques 35

Co-authors: Michael J. Pazzani (15 shared papers)Chotirat Ann Ratanamahatana (15 shared papers)Jessica Lin (18 shared papers)Stefano Lonardi (19 shared papers)Abdullah Mueen (29 shared papers)Wei Li (19 shared papers)Bill Chiu (5 shared papers)Sharad Mehrotra (4 shared papers)
Journals: Data Mining and Knowledge Discovery (31 papers)Knowledge and Information Systems (14 papers)Proceedings of the VLDB Endowment (4 papers)The VLDB Journal (3 papers)IEEE Transactions on Knowledge and Data Engineering (3 papers)
Partner nations: United States Brazil Thailand

In The Last Decade

Eamonn Keogh

266 papers receiving 25.6k citations

Eamonn Keogh's Hit Papers

15 papers align trajectories log scale

Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets 2016 · 358 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2016 Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets
2016 Data Mining and Knowledge Discovery
2012 PubMed
2012 Data Mining and Knowledge Discovery
2009 Time series shapelets
2008 Proceedings of the VLDB Endowment
2007 Data Mining and Knowledge Discovery
2006 Fast time series classification using numerosity reduction
2006 HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
2004 Knowledge and Information Systems
2003 Data Mining and Knowledge Discovery
2003 A symbolic representation of time series, with implications for streaming algorithms
2002 Very Large Data Bases
2002 An online algorithm for segmenting time series
2001 Locally adaptive dimensionality reduction for indexing large time series databases
2001 Knowledge and Information Systems
2001 Derivative Dynamic Time Warping
2000 Scaling up dynamic time warping for datamining applications

Peers

Countries citing papers authored by Eamonn Keogh

Since Specialization

Citations

This map shows the geographic impact of Eamonn Keogh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Eamonn Keogh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eamonn Keogh more than expected).

Fields of papers citing papers by Eamonn Keogh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eamonn Keogh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Eamonn Keogh. The network helps show where Eamonn Keogh may publish in the future.

Co-authors

The 25 scholars most cited alongside Eamonn Keogh, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Eamonn Keogh Line = papers co-authored together Eamonn Keogh links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 271 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Exact indexing of dynamic time warping Knowledge and Information Systems ·Eamonn Keogh, Chotirat Ann Ratanamahatana Hit paper breakdown →	2004	1314
2	A symbolic representation of time series, with implications for streaming algorithms Jessica Lin, Eamonn Keogh, Stefano Lonardi, Bill Chiu Hit paper breakdown →	2003	1119
3	Experiencing SAX: a novel symbolic representation of time series Data Mining and Knowledge Discovery ·Jessica Lin, Eamonn Keogh, Wei Li, Stefano Lonardi Hit paper breakdown →	2007	1023
4	Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases Knowledge and Information Systems ·Eamonn Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra Hit paper breakdown →	2001	1006
5	Querying and mining of time series data Proceedings of the VLDB Endowment ·Hui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, Eamonn Keogh Hit paper breakdown →	2008	912
6	The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances Data Mining and Knowledge Discovery ·Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large, Eamonn Keogh Hit paper breakdown →	2016	828
7	Derivative Dynamic Time Warping Eamonn Keogh, Michael J. Pazzani Hit paper breakdown →	2001	746
8	An online algorithm for segmenting time series Eamonn Keogh, S. Chu, David M. Hart, Michael J. Pazzani Hit paper breakdown →	2002	729
9	Searching and mining trillions of time series subsequences under dynamic time warping PubMed ·Thanawin Rakthanmanon, Bilson Campana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria, Eamonn Keogh Hit paper breakdown →	2012	686
10	Time series shapelets Lexiang Ye, Eamonn Keogh Hit paper breakdown →	2009	592
11	On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration Data Mining and Knowledge Discovery ·Eamonn Keogh, Shruti Kasetty Hit paper breakdown →	2003	581
12	Experimental comparison of representation methods and distance measures for time series data Data Mining and Knowledge Discovery ·Xiaoyue Wang, Abdullah Mueen, Hui Ding, Goce Trajcevski, Peter Scheuermann, Eamonn Keogh Hit paper breakdown →	2012	558
13	Locally adaptive dimensionality reduction for indexing large time series databases Eamonn Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra Hit paper breakdown →	2001	552
14	Scaling up dynamic time warping for datamining applications Eamonn Keogh, Michael J. Pazzani Hit paper breakdown →	2000	520
15	HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence Eamonn Keogh, Jessica Lin, A.W.-C. Fu Hit paper breakdown →	2006	461
16	A symbolic representation of time series, with implications for streaming algorithms Jessica Lin, Eamonn Keogh, Stefano Lonardi, Bill Chiu	2003	454
17	Exact indexing of dynamic time warping Very Large Data Bases ·Eamonn Keogh Hit paper breakdown →	2002	448
18	Fast time series classification using numerosity reduction Xiaopeng Xi, Eamonn Keogh, Christian R. Shelton, Wei Li, Chotirat Ann Ratanamahatana Hit paper breakdown →	2006	398
19	Probabilistic discovery of time series motifs Bill Chiu, Eamonn Keogh, Stefano Lonardi	2003	375
20	Towards parameter-free data mining Eamonn Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana	2004	361

About Eamonn Keogh

Eamonn Keogh is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Economics and Econometrics and Computer Networks and Communications, having authored 271 papers that have together received 27.0k indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (210 papers), Anomaly Detection Techniques and Applications (78 papers), Music and Audio Processing (74 papers), Data Management and Algorithms (73 papers), Complex Systems and Time Series Analysis (53 papers), Advanced Text Analysis Techniques (35 papers), Data Visualization and Analytics (24 papers) and Image Retrieval and Classification Techniques (22 papers). The work is most often cited by research in Signal Processing (19.1k citations), Artificial Intelligence (13.8k citations), Computer Vision and Pattern Recognition (4.3k citations), Economics and Econometrics (4.1k citations) and Computer Networks and Communications (2.4k citations). Eamonn Keogh has collaborated with scholars based in United States, Brazil and Thailand. Frequent co-authors include Michael J. Pazzani, Chotirat Ann Ratanamahatana, Jessica Lin, Stefano Lonardi, Abdullah Mueen, Wei Li, Bill Chiu, Sharad Mehrotra, Kaushik Chakrabarti and Lexiang Ye. Their work appears in journals such as Data Mining and Knowledge Discovery, Knowledge and Information Systems, Proceedings of the VLDB Endowment, The VLDB Journal and IEEE Transactions on Knowledge and Data Engineering.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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Eamonn Keogh

Eamonn Keogh's Hit Papers

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All Works

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