Eamonn Keogh
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
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- Time Series Analysis and Forecasting 251
- Data Management and Algorithms 93
- Music and Audio Processing 83
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- Anomaly Detection Techniques and Applications 94
- Advanced Text Analysis Techniques 41
- Co-authors
- Michael J. Pazzani (18 shared papers)Chotirat Ann Ratanamahatana (22 shared papers)Jessica Lin (27 shared papers)Stefano Lonardi (22 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)IEEE Transactions on Knowledge and Data Engineering (3 papers)The VLDB Journal (3 papers)
- Partner nations
- United StatesBrazilThailand
In The Last Decade
Eamonn Keogh
319 papers receiving 31.9k citations
Eamonn Keogh's Hit Papers
Peers
Comparison fields: 5 of 202
- Signal Processing 24.1k
- Artificial Intelligence 17.4k
- Computer Vision and Pattern Recognition 5.5k
- Economics and Econometrics 5.2k
- Computer Networks and Communications 3.0k
Countries citing papers authored by Eamonn Keogh
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
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.
All Works
Showing the 20 most-cited of 326 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Exact indexing of dynamic time warping Hit paper breakdown → | 2004 | 1491 |
| 2 | A symbolic representation of time series, with implications for streaming algorithms Hit paper breakdown → | 2003 | 1350 |
| 3 | Experiencing SAX: a novel symbolic representation of time series Hit paper breakdown → | 2007 | 1189 |
| 4 | Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases Hit paper breakdown → | 2001 | 1186 |
| 5 | Querying and mining of time series data Hit paper breakdown → | 2008 | 1046 |
| 6 | The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances Hit paper breakdown → | 2016 | 944 |
| 7 | An online algorithm for segmenting time series Hit paper breakdown → | 2002 | 851 |
| 8 | Derivative Dynamic Time Warping Hit paper breakdown → | 2001 | 839 |
| 9 | Searching and mining trillions of time series subsequences under dynamic time warping Hit paper breakdown → | 2012 | 774 |
| 10 | Time series shapelets Hit paper breakdown → | 2009 | 702 |
| 11 | Locally adaptive dimensionality reduction for indexing large time series databases Hit paper breakdown → | 2001 | 680 |
| 12 | On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration Hit paper breakdown → | 2003 | 673 |
| 13 | Experimental comparison of representation methods and distance measures for time series data Hit paper breakdown → | 2012 | 628 |
| 14 | Scaling up dynamic time warping for datamining applications Hit paper breakdown → | 2000 | 619 |
| 15 | HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence Hit paper breakdown → | 2006 | 564 |
| 16 | Exact indexing of dynamic time warping Hit paper breakdown → | 2002 | 525 |
| 17 | Fast time series classification using numerosity reduction Hit paper breakdown → | 2006 | 490 |
| 18 | 2003 | 489 | |
| 19 | SEGMENTING TIME SERIES: A SURVEY AND NOVEL APPROACH Hit paper breakdown → | 2004 | 478 |
| 20 | Probabilistic discovery of time series motifs Hit paper breakdown → | 2003 | 467 |
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 326 papers that have together received 33.6k indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (251 papers), Anomaly Detection Techniques and Applications (94 papers), Data Management and Algorithms (93 papers), Music and Audio Processing (83 papers), Complex Systems and Time Series Analysis (61 papers), Advanced Text Analysis Techniques (41 papers), Data Visualization and Analytics (25 papers) and Advanced Database Systems and Queries (23 papers). The work is most often cited by research in Signal Processing (24.1k citations), Artificial Intelligence (17.4k citations), Computer Vision and Pattern Recognition (5.5k citations), Economics and Econometrics (5.2k citations) and Computer Networks and Communications (3.0k 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, IEEE Transactions on Knowledge and Data Engineering and The VLDB Journal.
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.