Eamonn J. Keogh

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Time Series

Eamonn J. Keogh, 2017, Encyclopedia of Machine Learning and Data Mining.

Irrevocable-choice algorithms for sampling from a stream

Eamonn J. Keogh, Yan Zhu, Yan Zhu, 2016, Data Mining and Knowledge Discovery.

A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy pdf

Jun Wang, Eamonn J. Keogh, Nurjahan Begum, 2016, ArXiv.

Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems

Eamonn J. Keogh, Haifeng Chen, Kai Zhang, 2015, KDD.

Finding the most unusual time series subsequence: algorithms and applications

Eamonn J. Keogh, Jessica Lin, Sang-Hee Lee, 2006, Knowledge and Information Systems.

Indexing and Mining Time Series Data

Eamonn J. Keogh, 2008, Encyclopedia of GIS.

Eamonn J. Keogh, Gustavo E. A. P. A. Batista, Diego Furtado Silva, 2018, Data Mining and Knowledge Discovery.

Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm

Geoffrey I. Webb, Germain Forestier, Ann E. Nicholson, 2015, Knowledge and Information Systems.

Annotating historical archives of images

Eamonn J. Keogh, Christian R. Shelton, Xiaoyue Wang, 2008, Int. J. Digit. Libr. Syst..

Quantitative Analysis of Live-Cell Growth at the Shoot Apex of Arabidopsis thaliana: Algorithms for Feature Measurement and Temporal Alignment

Amit K. Roy-Chowdhury, Eamonn J. Keogh, Oben M. Tataw, 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Compression-Based Data Mining

Eamonn J. Keogh, Li Keogh, John Handley, 2009, Encyclopedia of Data Warehousing and Mining.

Exploring Low Cost Laser Sensors to Identify Flying Insect Species

Daniel P. W. Ellis, Eamonn J. Keogh, Vinícius M. A. de Souza, 2015, J. Intell. Robotic Syst..

Visualization and Mining of Temporal Data

Eamonn J. Keogh, 2005, IEEE Visualization.

Query Suggestion to allow Intuitive Interactive Search in Multidimensional Time Series

Eamonn J. Keogh, Yifei Ding, Yifei Ding, 2017, SSDBM.

Data Editing Techniques to Allow the Application of Distance-Based Outlier Detection to Streams

Eamonn J. Keogh, Vit Niennattrakul, Chotirat Ratanamahatana, 2010, 2010 IEEE International Conference on Data Mining.

Augmenting the generalized hough transform to enable the mining of petroglyphs

Eamonn J. Keogh, Sang-Hee Lee, Qiang Zhu, 2009, KDD.

An indexing scheme for fast similarity search in large time series databases

Eamonn J. Keogh, Michael J. Pazzani, 1999, Proceedings. Eleventh International Conference on Scientific and Statistical Database Management.

Time Series Classification under More Realistic Assumptions

Eamonn J. Keogh, Yanping Chen, Bing Hu, 2013, SDM.

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

Hui Ding, Eamonn J. Keogh, Peter Scheuermann, 2008, Proc. VLDB Endow..

A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases

Eamonn J. Keogh, Michael J. Pazzani, M. Pazzani, 2000, PAKDD.

Accelerating Time Series Searching with Large Uniform Scaling

Hongxia Jin, Eamonn J. Keogh, Yilin Shen, 2018, SDM.

A Minimum Description Length Technique for Semi-Supervised Time Series Classification

Eamonn J. Keogh, Nurjahan Begum, Bing Hu, 2013, IRI.

Ensembles of Nearest Neighbor Forecasts

Eamonn J. Keogh, Dragomir Yankov, Dennis DeCoste, 2006, ECML.

Indexing and Mining Time Series Data

Eamonn J. Keogh, 2017, Encyclopedia of GIS.

Data Mining a Trillion Time Series Subsequences Under Dynamic Time Warping

Eamonn J. Keogh, Thanawin Rakthanmanon, 2013, IJCAI.

Mining Historical Documents for Near-Duplicate Figures

Eamonn J. Keogh, Qiang Zhu, Thanawin Rakthanmanon, 2011, 2011 IEEE 11th International Conference on Data Mining.

Mining Time Series Data

Dimitrios Gunopulos, Eamonn J. Keogh, Jessica Lin, 2005, Data Mining and Knowledge Discovery Handbook.

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

Eamonn J. Keogh, Lexiang Ye, Lexiang Ye, 2010, Data Mining and Knowledge Discovery.

FastDTW is approximate and Generally Slower than the Algorithm it Approximates pdf

Eamonn J. Keogh, Renjie Wu, 2020, ArXiv.

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

Eamonn J. Keogh, Chin-Chia Michael Yeh, Zachary Schall-Zimmerman, 2018, 2018 IEEE International Conference on Data Mining (ICDM).

A Compression Based Distance Measure for Texture

Eamonn J. Keogh, Bilson J. L. Campana, 2010, SDM.

Scaling up Dynamic Time Warping to Massive Dataset

Eamonn J. Keogh, Michael J. Pazzani, M. Pazzani, 1999, PKDD.

Eamonn J. Keogh, 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

Generalized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances

Elke A. Rundensteiner, Gábor N. Sárközy, Eamonn J. Keogh, 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

Indexing Multidimensional Time-Series

Dimitrios Gunopulos, Marios Hadjieleftheriou, Eamonn J. Keogh, 2004, The VLDB Journal.

An online algorithm for segmenting time series

Eamonn J. Keogh, Michael J. Pazzani, David M. Hart, 2001, Proceedings 2001 IEEE International Conference on Data Mining.

A brief survey on sequence classification

Jian Pei, Eamonn J. Keogh, Zhengzheng Xing, 2010, SKDD.

Detecting time series motifs under uniform scaling

Eamonn J. Keogh, Victor B. Zordan, Dragomir Yankov, 2007, KDD '07.

Time Series Classification to Improve Poultry Welfare

Eamonn J. Keogh, Chin-Chia Michael Yeh, Alireza Abdoli, 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

DTW-D: time series semi-supervised learning from a single example

Eamonn J. Keogh, Gustavo E. A. P. A. Batista, Yanping Chen, 2013, KDD.

A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets

Eamonn J. Keogh, Qiang Zhu, Gustavo E. A. P. A. Batista, 2012, SDM.

Manifold Clustering of Shapes

Eamonn J. Keogh, Dragomir Yankov, Dragomir Yankov, 2006, Sixth International Conference on Data Mining (ICDM'06).

Indexing and Mining Time Series

Eamonn J. Keogh, 2002, SBBD.

Domain agnostic online semantic segmentation for multi-dimensional time series

Eamonn J. Keogh, Wei Ding, Shaghayegh Gharghabi, 2018, Data Mining and Knowledge Discovery.

Instruction set extensions for Dynamic Time Warping

Eamonn J. Keogh, Philip Brisk, Joseph Tarango, 2013, 2013 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining

Dah-Jye Lee, Eamonn J. Keogh, Xiaopeng Xi, 2006, Sixth International Conference on Data Mining (ICDM'06).

Guest Editorial

Eamonn J. Keogh, 2005, Machine Learning.

Establishing the provenance of historical manuscripts with a novel distance measure

Eamonn J. Keogh, Bing Hu, Abdullah Mueen, 2013, Pattern Analysis and Applications.

Derivative Dynamic Time Warping

Eamonn J. Keogh, Michael J. Pazzani, M. Pazzani, 2001, SDM.

Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times

Eamonn J. Keogh, Jin Shieh, 2010, 2010 IEEE International Conference on Data Mining.

Monitoring and Mining Insect Sounds in Visual Space

Eamonn J. Keogh, Yuan Hao, Bilson J. L. Campana, 2012, SDM.

Knowledge and Information Systems REGULAR PAPER

Eamonn J. Keogh, Russ Abbott, Helga Van Herle, 2007 .

A Bit Level Representation for Time Series Data Mining with Shape Based Similarity

Gareth J. Janacek, Eamonn J. Keogh, Stefano Lonardi, 2006, Data Mining and Knowledge Discovery.

Representation Learning by Reconstructing Neighborhoods pdf

Eamonn J. Keogh, Yan Zhu, Evangelos E. Papalexakis, 2018, ArXiv.

WAT: Finding Top-K Discords in Time Series Database

Jian Pei, Eamonn J. Keogh, Ada Wai-Chee Fu, 2007, SDM.

Learning the Structure of Augmented Bayesian Classifiers

Eamonn J. Keogh, Michael J. Pazzani, 2002, Int. J. Artif. Intell. Tools.

Ensemble-index: a new approach to indexing large databases

Eamonn J. Keogh, Michael J. Pazzani, Selina Chu, 2001, KDD '01.

Real-Time Classification of Streaming Sensor Data

Eamonn J. Keogh, Xiaoyue Wang, Shashwati Kasetty, 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.

An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback

Eamonn J. Keogh, Michael J. Pazzani, M. Pazzani, 1998, KDD.

Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins

Eamonn J. Keogh, Philip Brisk, Chin-Chia Michael Yeh, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

Online amnesic approximation of streaming time series

Dimitrios Gunopulos, Eamonn J. Keogh, Themis Palpanas, 2004, Proceedings. 20th International Conference on Data Engineering.

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

Eamonn J. Keogh, Yan Zhu, Nurjahan Begum, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining

Eamonn J. Keogh, Dragomir Yankov, Xiaoyue Wang, 2008, SDM.

A Clustering Analysis for Target Group Identification by Locality in Motor Insurance Industry

Eamonn J. Keogh, Xiaozhe Wang, 2008, Soft Computing Applications in Business.

Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL

Eamonn J. Keogh, Stefano Lonardi, Yuan Hao, 2011, 2011 IEEE 11th International Conference on Data Mining.

Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels

Eamonn J. Keogh, Liudmila Ulanova, Shaghayegh Gharghabi, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

Using CAPTCHAs to Index Cultural Artifacts

Eamonn J. Keogh, Qiang Zhu, 2010, IDA.

Prefix and Suffix Invariant Dynamic Time Warping

Eamonn J. Keogh, Gustavo E. A. P. A. Batista, Diego Furtado Silva, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

(Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research

Eamonn J. Keogh, Jessica Lin, Wagner Truppel, 2003, IC-AI.

VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases

Eamonn J. Keogh, Stefano Lonardi, Jessica Lin, 2004, VLDB.

Clustering Workflow Requirements Using Compression Dissimilarity Measure

Eamonn J. Keogh, John Handley, Li Wei, 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

A Probabilistic Approach to Fast Pattern Matching in Time Series Databases

Eamonn J. Keogh, Padhraic Smyth, Padhraic Smyth, 1997, KDD.

Parameter-Free Audio Motif Discovery in Large Data Archives

Eamonn J. Keogh, Mohammad Shokoohi-Yekta, George Papageorgiou, 2013, 2013 IEEE 13th International Conference on Data Mining.

Scalable Clustering of Time Series with U-Shapelets

Eamonn J. Keogh, Nurjahan Begum, Liudmila Ulanova, 2015, SDM.

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

Eamonn J. Keogh, James Large, Jason Lines, 2016, Data Mining and Knowledge Discovery.

Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases

Eamonn J. Keogh, Chotirat Ratanamahatana, C. Ratanamahatana, 2005, KES.

Finding Motifs in a Database of Shapes

Eamonn J. Keogh, Xiaopeng Xi, Li Wei, 2007, SDM.

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

Eamonn J. Keogh, Shruti Kasetty, Shruti Kasetty, 2002, Data Mining and Knowledge Discovery.

Configurable cache subsetting for fast cache tuning

Frank Vahid, Ann Gordon-Ross, Eamonn J. Keogh, 2006, 2006 43rd ACM/IEEE Design Automation Conference.

Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series

Eamonn J. Keogh, Stefano Lonardi, Yuan Hao, 2014, Data Mining and Knowledge Discovery.

VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series

Eamonn J. Keogh, Yan Zhu, Themis Palpanas, 2018, SIGMOD Conference.

Visual Exploration of Genomic Data

Philip S. Yu, Eamonn J. Keogh, Michail Vlachos, 2007, PKDD.

Curse of Dimensionality

Eamonn J. Keogh, Abdullah Mueen, 2010, Encyclopedia of Machine Learning.

Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series

Eamonn J. Keogh, Yan Zhu, Themis Palpanas, 2018, SIGMOD Conference.

Clustering of streaming time series is meaningless

Eamonn J. Keogh, Jessica Lin, Wagner Truppel, 2003, DMKD '03.

On the Non-Trivial Generalization of Dynamic Time Warping to the Multi-Dimensional Case

Jun Wang, Eamonn J. Keogh, Mohammad Shokoohi-Yekta, 2015, SDM.

Rare Time Series Motif Discovery from Unbounded Streams

Eamonn J. Keogh, Nurjahan Begum, Nurjahan Begum, 2014, Proc. VLDB Endow..

Natura: Towards Conversational Analytics for Comparing and Contrasting Time Series

Eamonn J. Keogh, Shima Imani, 2020, WWW.

Classification of Live Moths Combining Texture, Color and Shape Primitives

Eamonn J. Keogh, Gustavo E. A. P. A. Batista, Bilson J. L. Campana, 2010, 2010 Ninth International Conference on Machine Learning and Applications.

Partial elastic matching of time series

Qiang Wang, Longin Jan Latecki, Eamonn J. Keogh, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams

Dimitrios Gunopulos, Eamonn J. Keogh, Jessica Lin, 2005, PAKDD.

Logical-shapelets: an expressive primitive for time series classification

Eamonn J. Keogh, Neal E. Young, Abdullah Mueen, 2011, KDD.

Exploiting a novel algorithm and GPUs to break the ten quadrillion pairwise comparisons barrier for time series motifs and joins

Eamonn J. Keogh, Philip Brisk, Chin-Chia Michael Yeh, 2017, Knowledge and Information Systems.

Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile

Eamonn J. Keogh, Nurjahan Begum, Liudmila Ulanova, 2017, Data Mining and Knowledge Discovery.

Finding Time Series Discords Based on Haar Transform

Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu, 2006, ADMA.

Matrix Profile XIV: Scaling Time Series Motif Discovery with GPUs to Break a Quintillion Pairwise Comparisons a Day and Beyond

Eamonn J. Keogh, Philip Brisk, Zachary Schall-Zimmerman, 2019, SoCC.

We Have Seen the Future, and It Is Symbolic

Eamonn J. Keogh, Stefano Lonardi, Jessica Lin, 2004, ACSW.

Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches

Eamonn J. Keogh, Michael J. Pazzani, M. Pazzani, 1999, AISTATS.

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

Eamonn J. Keogh, Renjie Wu, 2020, ArXiv.

A compression‐based distance measure for texture

Eamonn J. Keogh, Bilson J. L. Campana, 2010, Stat. Anal. Data Min..

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

Eamonn J. Keogh, Wagner Truppel, Jessica Lin, 2003, Third IEEE International Conference on Data Mining.

Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs

Eamonn J. Keogh, Vit Niennattrakul, Walid A. Najjar, 2010, 2010 IEEE International Conference on Data Mining.

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

Eamonn J. Keogh, Stefano Lonardi, Jessica Lin, 2003, DMKD '03.

Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases

Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani, 2001, Knowledge and Information Systems.

How to Do Good Data Mining Research and Get it Published in Top Venues

Eamonn J. Keogh, 2010, 2010 IEEE International Conference on Data Mining.

Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios

Eamonn J. Keogh, Anthony J. Bagnall, Shaghayegh Gharghabi, 2018, 2018 IEEE International Conference on Data Mining (ICDM).

Iterative Deepening Dynamic Time Warping for Time Series

Eamonn J. Keogh, Michael J. Pazzani, David M. Hart, 2002, SDM.

A Complexity-Invariant Distance Measure for Time Series

Eamonn J. Keogh, Gustavo E. A. P. A. Batista, Xiaoyue Wang, 2011, SDM.

Grid-Based Indexing for Large Time Series Databases

Eamonn J. Keogh, Jiyuan An, Hanxiong Chen, 2003, IDEAL.

Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data

Eamonn J. Keogh, Stefano Lonardi, Scott Evans, 2011, 2011 IEEE 11th International Conference on Data Mining.

HOT SAX: efficiently finding the most unusual time series subsequence

Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

Recent Advances in Mining Time Series Data

Eamonn J. Keogh, 2005, PKDD.

Classification of streaming time series under more realistic assumptions

Eamonn J. Keogh, Yanping Chen, Bing Hu, 2015, Data Mining and Knowledge Discovery.

A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering

Eamonn J. Keogh, Stefano Lonardi, Anthony J. Bagnall, 2005, PAKDD.

Finding centuries-old hyperlinks with a novel semi-supervised learning technique

Eamonn J. Keogh, Xiaoyue Wang, 2009, JCDL '09.

Relevance feedback retrieval of time series data

Eamonn J. Keogh, Michael J. Pazzani, M. Pazzani, 1999, SIGIR '99.

A Grid-Based Index Method for Time Warping Distance

Eamonn J. Keogh, Jiyuan An, Yi-Ping Phoebe Chen, 2004, WAIM.

Matrix Profile VI: Meaningful Multidimensional Motif Discovery

Eamonn J. Keogh, Chin-Chia Michael Yeh, Nickolas Kavantzas, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

Efficiently Finding Near Duplicate Figures in Archives of Historical Documents

Eamonn J. Keogh, Qiang Zhu, Thanawin Rakthanmanon, 2012, J. Multim..

iSAX 2.0: Indexing and Mining One Billion Time Series

Eamonn J. Keogh, Themis Palpanas, Jin Shieh, 2010, 2010 IEEE International Conference on Data Mining.

Scaling and time warping in time series querying

Eamonn J. Keogh, Ada Wai-Chee Fu, Chotirat Ratanamahatana, 2005, The VLDB Journal.

Probabilistic discovery of time series motifs

Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu, 2003, KDD '03.

Approximations to magic: finding unusual medical time series

Eamonn J. Keogh, Jessica Lin, Helga Van Herle, 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

Time series shapelets: a new primitive for data mining

Eamonn J. Keogh, Lexiang Ye, Lexiang Ye, 2009, KDD.

Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications

Eamonn J. Keogh, Jessica Lin, Helga Van Herle, 2006, IEEE Transactions on Information Technology in Biomedicine.

Mother Fugger: Mining Historical Manuscripts with Local Color Patches

Eamonn J. Keogh, Qiang Zhu, 2010, 2010 IEEE International Conference on Data Mining.

Locally Constrained Support Vector Clustering

Eamonn J. Keogh, Dragomir Yankov, Kin Fai Kan, 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

Finding surprising patterns in a time series database in linear time and space

Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu, 2002, KDD.

Towards a minimum description length based stopping criterion for semi-supervised time series classification

Eamonn J. Keogh, Nurjahan Begum, Bing Hu, 2013, 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI).

Iterative Incremental Clustering of Time Series

Dimitrios Gunopulos, Eamonn J. Keogh, Jessica Lin, 2004, EDBT.

Data mining and information retrieval in time series/multimedia databases

Eamonn J. Keogh, 2006, MM '06.

Clustering of Symbols Using Minimal Description Length

Eamonn J. Keogh, Oben M. Tataw, Thanawin Rakthanmanon, 2013, 2013 12th International Conference on Document Analysis and Recognition.

Searching historical manuscripts for near-duplicate figures

Eamonn J. Keogh, Qiang Zhu, Thanawin Rakthanmanon, 2011, HIP '11.

Time Series Chains: A Novel Tool for Time Series Data Mining

Eamonn J. Keogh, Yan Zhu, Daniel Nikovski, 2018, IJCAI.

Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL

Eamonn J. Keogh, Stefano Lonardi, Yuan Hao, 2011, 2011 IEEE 11th International Conference on Data Mining.

Indexing Large Human-Motion Databases

Dimitrios Gunopulos, Eamonn J. Keogh, Victor B. Zordan, 2004, VLDB.

Autocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data Mining

Eamonn J. Keogh, Xiaoyue Wang, Lexiang Ye, 2009, SDM.

Nearest Neighbor

Eamonn J. Keogh, 2010, Encyclopedia of Machine Learning.

Three Myths about Dynamic Time Warping Data Mining

Eamonn J. Keogh, Chotirat Ratanamahatana, C. Ratanamahatana, 2005, SDM.

SiMPle: Assessing Music Similarity Using Subsequences Joins

Eamonn J. Keogh, Gustavo E. A. P. A. Batista, Diego Furtado Silva, 2016, ISMIR.

Online discovery and maintenance of time series motifs

Eamonn J. Keogh, Abdullah Mueen, A. Mueen, 2010, KDD.

Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series

Germain Forestier, Eamonn J. Keogh, Hoang Anh Dau, 2017, 2017 IEEE International Conference on Big Data (Big Data).

An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data

Ben Shneiderman, Harry Hochheiser, Eamonn J. Keogh, 2002, FQAS.

Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases

Eamonn J. Keogh, 2003, PKDD.

Discovery of Meaningful Rules in Time Series

Eamonn J. Keogh, Mohammad Shokoohi-Yekta, Yanping Chen, 2015, KDD.

Domain-Driven, Actionable Knowledge Discovery

David Taniar, Philip S. Yu, Chengqi Zhang, 2007, IEEE Intelligent Systems.

Exact Discovery of Time Series Motifs

Eamonn J. Keogh, Qiang Zhu, M. Brandon Westover, 2009, SDM.

Disk aware discord discovery: finding unusual time series in terabyte sized datasets

Eamonn J. Keogh, Umaa Rebbapragada, Dragomir Yankov, 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

Indexing multi-dimensional time-series with support for multiple distance measures

Dimitrios Gunopulos, Marios Hadjieleftheriou, Eamonn J. Keogh, 2003, KDD '03.

Classification of Multi-dimensional Streaming Time Series by Weighting Each Classifier's Track Record

Eamonn J. Keogh, Liudmila Ulanova, Yanping Chen, 2013, 2013 IEEE 13th International Conference on Data Mining.

The UCR time series archive

Eamonn J. Keogh, Yan Zhu, Hoang Anh Dau, 2018, IEEE/CAA Journal of Automatica Sinica.

Optimizing dynamic time warping’s window width for time series data mining applications

Germain Forestier, Eamonn J. Keogh, Hoang Anh Dau, 2018, Data Mining and Knowledge Discovery.

Data, Mining Time Series Data

Eamonn J. Keogh, 2011, International Encyclopedia of Statistical Science.

Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes

Eamonn J. Keogh, Chin-Chia Michael Yeh, Nickolas Kavantzas, 2017, Proc. VLDB Endow..

A general framework for never-ending learning from time series streams

Eamonn J. Keogh, Yuan Hao, Yanping Chen, 2015, Data Mining and Knowledge Discovery.

iSAX: disk-aware mining and indexing of massive time series datasets

Eamonn J. Keogh, Jin Shieh, Jin Shieh, 2009, Data Mining and Knowledge Discovery.

Efficient Discovery of Unusual Patterns in Time Series

Eamonn J. Keogh, Stefano Lonardi, Jessica Lin, 2006, New Generation Computing.

TS2-tree - an efficient similarity based organization for trajectory data

Petko Bakalov, Eamonn J. Keogh, Vassilis J. Tsotras, 2007, GIS.

Generating Synthetic Data to Allow Learning from a Single Exemplar per Class

Eamonn J. Keogh, Liudmila Ulanova, Yuan Hao, 2014, SISAP.

Reliable early classification of time series based on discriminating the classes over time

Eamonn J. Keogh, Alexander Mendiburu, José Antonio Lozano, 2016, Data Mining and Knowledge Discovery.

The UEA multivariate time series classification archive, 2018 pdf

Michael Flynn, Eamonn J. Keogh, Hoang Anh Dau, 2018, ArXiv.

Diversifying query results on semi-structured data

Eamonn J. Keogh, Vassilis J. Tsotras, Abdullah Mueen, 2012, CIKM '12.

Mining Massive Archives of Mice Sounds with Symbolized Representations

Eamonn J. Keogh, Abdullah Mueen, Jesin Zakaria, 2012, SDM.

Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases

Nitin Kumar, Eamonn J. Keogh, Stefano Lonardi, 2005, SDM.

Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases

Eamonn J. Keogh, Stefano Lonardi, Jessica Lin, 2005, Inf. Vis..

Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining (Best Student Paper Award)

Eamonn J. Keogh, Yan Zhu, Daniel Nikovski, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

Matrix Profile XX: Finding and Visualizing Time Series Motifs of All Lengths using the Matrix Profile

Eamonn J. Keogh, Shima Imani, Zachary Schall-Zimmerman, 2019, 2019 IEEE International Conference on Big Knowledge (ICBK).

Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior

Eamonn J. Keogh, Alireza Abdoli, Amy C. Murillo, 2019, 2019 IEEE International Conference on Big Data (Big Data).

Visually mining and monitoring massive time series

Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, 2004, KDD.

Image Mining of Historical Manuscripts to Establish Provenance

Eamonn J. Keogh, Bing Hu, Abdullah Mueen, 2012, SDM.

Making Time-Series Classification More Accurate Using Learned Constraints

Eamonn J. Keogh, Chotirat Ratanamahatana, C. Ratanamahatana, 2004, SDM.

Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile

Eamonn J. Keogh, Evangelos E. Papalexakis, Philip Brisk, 2019, 2019 IEEE International Conference on Data Mining (ICDM).

Streaming Time Series Summarization Using User-Defined Amnesic Functions

Dimitrios Gunopulos, Eamonn J. Keogh, Themis Palpanas, 2008, IEEE Transactions on Knowledge and Data Engineering.

Towards never-ending learning from time series streams

Eamonn J. Keogh, Yuan Hao, Yanping Chen, 2013, KDD.

Scaling up dynamic time warping for datamining applications

Eamonn J. Keogh, Michael J. Pazzani, M. Pazzani, 2000, KDD '00.

Searching Time Series with Invariance to Large Amounts of Uniform Scaling

Hongxia Jin, Eamonn J. Keogh, Yilin Shen, 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

Beyond one billion time series: indexing and mining very large time series collections with $$i$$SAX2+

Eamonn J. Keogh, Themis Palpanas, Jin Shieh, 2013, Knowledge and Information Systems.

Accelerating the discovery of unsupervised-shapelets

Eamonn J. Keogh, Neal E. Young, Abdullah Mueen, 2015, Data Mining and Knowledge Discovery.

A Gentle Introduction to Machine Learning and Data Mining for the Database Community

Eamonn J. Keogh, 2003, SBBD.

Admissible Time Series Motif Discovery with Missing Data pdf

Eamonn J. Keogh, Yan Zhu, Abdullah Mueen, 2018, ArXiv.

Elastic Partial Matching of Time Series

Qiang Wang, Longin Jan Latecki, Eamonn J. Keogh, 2005, PKDD.

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

Eamonn J. Keogh, Jessica Lin, Jessica Lin, 2004, Knowledge and Information Systems.

Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets

Eamonn J. Keogh, Thanawin Rakthanmanon, T. Rakthanmanon, 2013, SDM.

Towards parameter-free data mining

Eamonn J. Keogh, Stefano Lonardi, Chotirat Ratanamahatana, 2004, KDD.

Towards Automatic Classification on Flying Insects Using Inexpensive Sensors

Eamonn J. Keogh, Gustavo E. A. P. A. Batista, Yuan Hao, 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

Global distance-based segmentation of trajectories

Philip S. Yu, Marios Hadjieleftheriou, Eamonn J. Keogh, 2006, KDD '06.

Instance-Based Learning

Eamonn J. Keogh, 2010, Encyclopedia of Machine Learning.

Matrix Profile III: The Matrix Profile Allows Visualization of Salient Subsequences in Massive Time Series

Eamonn J. Keogh, Helga Van Herle, Chin-Chia Michael Yeh, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

Efficient trajectory joins using symbolic representations

Marios Hadjieleftheriou, Petko Bakalov, Eamonn J. Keogh, 2005, MDM '05.

A decade of progress in indexing and mining large time series databases

Eamonn J. Keogh, 2006, VLDB.

Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery

Eamonn J. Keogh, Hoang Anh Dau, 2017, KDD.

An efficient and effective similarity measure to enable data mining of petroglyphs

Eamonn J. Keogh, Sang-Hee Lee, Qiang Zhu, 2010, Data Mining and Knowledge Discovery.

Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping

Eamonn J. Keogh, Qiang Zhu, Gustavo E. A. P. A. Batista, 2013, TKDD.

Locally adaptive dimensionality reduction for indexing large time series databases

Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani, 2001, SIGMOD '01.

Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping

Eamonn J. Keogh, Nurjahan Begum, Hoang Anh Dau, 2016, CIKM.

Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets

Eamonn J. Keogh, Umaa Rebbapragada, Dragomir Yankov, 2007, ICDM.

A disk-aware algorithm for time series motif discovery

Eamonn J. Keogh, Qiang Zhu, M. Brandon Westover, 2011, Data Mining and Knowledge Discovery.

Atomic wedgie: efficient query filtering for streaming time series

Eamonn J. Keogh, Helga Van Herle, Li Wei, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps

Daniel P. W. Ellis, Eamonn J. Keogh, Vinícius M. A. de Souza, 2013, 2013 12th International Conference on Machine Learning and Applications.

Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data

Eamonn J. Keogh, Jessica Lin, Jessica Lin, 2006, PKDD.

Extracting Optimal Performance from Dynamic Time Warping

Eamonn J. Keogh, Abdullah Mueen, A. Mueen, 2016, KDD.

MDL-based time series clustering

Eamonn J. Keogh, Stefano Lonardi, Scott Evans, 2012, Knowledge and Information Systems.

Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping

Eamonn J. Keogh, Qiang Zhu, Gustavo E. A. P. A. Batista, 2012, KDD.

Matrix Profile XIII: Time Series Snippets: A New Primitive for Time Series Data Mining

Eamonn J. Keogh, Wei Ding, Scott E. Crouter, 2018, 2018 IEEE International Conference on Big Knowledge (ICBK).

Mining historical manuscripts with local color patches

Eamonn J. Keogh, Qiang Zhu, Qiang Zhu, 2011, Knowledge and Information Systems.

Dot plots for time series analysis

Eamonn J. Keogh, Stefano Lonardi, Ada Wai-Chee Fu, 2005, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05).

CID: an efficient complexity-invariant distance for time series

Eamonn J. Keogh, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista, 2013, Data Mining and Knowledge Discovery.

Multimedia Retrieval Using Time Series Representation and Relevance Feedback

Eamonn J. Keogh, Chotirat Ratanamahatana, 2005, ICADL.

Curse of Dimensionality

Eamonn J. Keogh, Abdullah Mueen, 2017, Encyclopedia of Machine Learning and Data Mining.

Finding Time Series Motifs in Disk-Resident Data

Eamonn J. Keogh, Nima Bigdely Shamlo, Abdullah Mueen, 2009, 2009 Ninth IEEE International Conference on Data Mining.

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

Eamonn J. Keogh, Goce Trajcevski, Peter Scheuermann, 2010, Data Mining and Knowledge Discovery.

Converting non-parametric distance-based classification to anytime algorithms

Dah-Jye Lee, Eamonn J. Keogh, Xiaopeng Xi, 2008, Pattern Analysis and Applications.

iSAX: indexing and mining terabyte sized time series

Eamonn J. Keogh, Jin Shieh, Jin Shieh, 2008, KDD.

Instance-Based Learning

Eamonn J. Keogh, 2010, Encyclopedia of Machine Learning and Data Mining.

Indexing Multi-Dimensional Trajectories for Similarity Queries

Dimitrios Gunopulos, Marios Hadjieleftheriou, Eamonn J. Keogh, 2005, Spatial Databases.

Nearest Neighbor

Eamonn J. Keogh, 2010, Encyclopedia of Machine Learning.

Augmenting Historical Manuscripts with Automatic Hyperlinks

Eamonn J. Keogh, Xiaoyue Wang, Xiaoyue Wang, 2009, 2009 11th IEEE International Symposium on Multimedia.

Generating Synthetic Time Series to Augment Sparse Datasets

Geoffrey I. Webb, Germain Forestier, Eamonn J. Keogh, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

Accelerating the dynamic time warping distance measure using logarithmetic arithmetic

Eamonn J. Keogh, Philip Brisk, Joseph Tarango, 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.

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

Jun Wang, Hongxia Jin, Eamonn J. Keogh, 2016, Data Mining and Knowledge Discovery.

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

Eamonn J. Keogh, Renjie Wu, R. Wu, 2020, IEEE Transactions on Knowledge and Data Engineering.

Time series motifs discovery under DTW allows more robust discovery of conserved structure

Eamonn J. Keogh, Sara Alaee, Kaveh Kamgar, 2021, Data Mining and Knowledge Discovery.

FastDTW is approximate and Generally Slower than the Algorithm it Approximates (Extended Abstract)

Eamonn J. Keogh, Renjie Wu, 2021, 2021 IEEE 37th International Conference on Data Engineering (ICDE).

When is Early Classification of Time Series Meaningful? (Extended Abstract) pdf

Eamonn J. Keogh, Renjie Wu, Audrey Der, 2021, 2022 IEEE 38th International Conference on Data Engineering (ICDE).

UCR Time Series Data Mining Archive

Eamonn J. Keogh, Theodoros Folias, 1983 .

A Wavelet-Based Anytime Algorithm for K-Means Clustering of Time Series

Dimitrios Gunopulos, Eamonn J. Keogh, Jessica Lin, 2003 .

Test Data

Risto Miikkulainen, Marko Grobelnik, Pierre Flener, 2019, Encyclopedia of Machine Learning and Data Mining.

Generalizing Dynamic Time Warping to the Multi-Dimensional Case Requires an Adaptive Approach

Eamonn J. Keogh, Hongxia Jin, Mohammad Shokoohi-Yekta, 2014 .

Mining motifs in massive time series databases

Eamonn J. Keogh, Stefano Lonardi, Jessica Lin, 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

Online Amnestic Dynamic Time Warping to Allow Real-Time Golden Batch Monitoring

Eamonn J. Keogh, Hoang Anh Dau, Chin-Chia Michael Yeh, 2019 .

Flying insect detection and classification with inexpensive sensors.

Eamonn J. Keogh, A. Mafra-Neto, Yanping Chen, 2014, Journal of visualized experiments : JoVE.

Using Relevance Feedback in Multimedia Databases

Eamonn J. Keogh, C. Ratanamahatana, 2004 .

The classic dynamic time warping algorithm

Eamonn J. Keogh, M. Pazzani, 2001 .

UNIVERSITY OF CALIFORNIA RIVERSIDE A Framework for Semi-Supervised Learning Based on Subjective and Objective Cluster Validity Criteria A Thesis submitted in partial satisfaction of the requirements for the degree of Master of Science

Eamonn J. Keogh, D. Gunopulos, Nitin Kumar, 2005 .

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

Eamonn J. Keogh, Takaaki Nakamura, Makoto Imamura, 2020, 2020 IEEE International Conference on Data Mining (ICDM).

Fast time series classification using numerosity reduction

Li Wei, Eamonn J. Keogh, Chotirat Ann Ratanamahatana, 2006, ICML.

Automatic Construction of Typologies for Massive Collections of Projectile Points

Eamonn J. Keogh, Lexiang Ye, Taryn Rampley, 2010 .

Scaling Time Series Motif Discovery with GPUs : Breaking the Quintillion Pairwise Comparisons a Day Barrier

Eamonn J. Keogh, G. Funning, Yan Zhu, 2018 .

Supporting anthropological research with efficient rotation invariant shape similarity measurement

Eamonn J. Keogh, X. Xi, L. Wei, 2007, Journal of The Royal Society Interface.

FINDING OR NOT FINDING RULES IN TIME SERIES

Eamonn J. Keogh, Jessica Lin, Eamonn Keogh, 2004 .

Algorithms for Learning Augmented Bayesian Classifiers

Eamonn J. Keogh, Michael J. Pazzani, 1999 .

Towards Indexing and Data Mining All the World’s Rock Art

Eamonn J. Keogh, Qiang Zhu, Xiaoyue Wang, 2010 .

Matrix profile goes MAD: variable-length motif and discord discovery in data series

Eamonn J. Keogh, Yan Zhu, Themis Palpanas, 2020, Data Mining and Knowledge Discovery.

PROGRESS IN THE TREATMENT OF BURNED CHILDREN

Eamonn J. Keogh, A. Clarke, A. M. Clarke, 1970, The Medical journal of Australia.

Click-Through Rate (CTR)

Jean-Michel Renders, João Gama, Eamonn J. Keogh, 2010, Encyclopedia of Machine Learning and Data Mining.

Efficient and Effective Labeling of Massive Entomological Datasets

Eamonn J. Keogh, Q. Chesnais, K. Mauck, 2019 .

Qute: Query by Text Search for Time Series Data

Eamonn J. Keogh, Shima Imani, S. Alaee, 2020 .

Parasitic mites alter chicken behaviour and negatively impact animal welfare

Eamonn J. Keogh, A. Gerry, A. Murillo, 2020, Scientific Reports.

On the need for time series data mining benchmarks: a survey and empirical demonstration

Eamonn J. Keogh, Shruti Kasetty, 2002, KDD.

Preface to the International Workshop on Spatial and Spatio-Temporal Data Mining

Dimitrios Gunopulos, Eamonn J. Keogh, Alfredo Cuzzocrea, 2011, IEEE ICDM 2011.

Complex Adaptive System

John Case, Jean-Michel Renders, Eamonn J. Keogh, 2010, Encyclopedia of Machine Learning.

Test Set

Risto Miikkulainen, Marko Grobelnik, Pierre Flener, 2010, Encyclopedia of Machine Learning and Data Mining.

Inverse Resolution

Pierre Flener, Eamonn J. Keogh, Ricardo Vilalta, 2010, Encyclopedia of Machine Learning and Data Mining.

Group SAX : Extending the Notion of Contrast Sets to Multimedia Data

Eamonn J. Keogh, Jessica Lin, 2006 .

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

Eamonn J. Keogh, Shruti Kasetty, 2002, Data Mining and Knowledge Discovery.

Indexing and Mining Time Series Data

Eamonn J. Keogh, 2008, Encyclopedia of GIS.

Finding the most unusual time series subsequence: algorithms and applications

Eamonn J. Keogh, Jessica Lin, Sang-Hee Lee, 2006, Knowledge and Information Systems.

Locally adaptive dimensionality reduction for indexing large time series databases

Eamonn J. Keogh, S. Mehrotra, M. Pazzani, 2001, TODS.

Matrix Profile Index Approximation for Streaming Time Series

Eamonn J. Keogh, Samet Oymak, E. Papalexakis, 2021, 2021 IEEE International Conference on Big Data (Big Data).

Locally adaptive dimensionality reduction for indexing large time series databases

Eamonn J. Keogh, S. Mehrotra, M. Pazzani, 2001, SIGMOD '01.

Optimizing dynamic time warping’s window width for time series data mining applications

Eamonn J. Keogh, Hoang Anh Dau, Diego Furtado Silva, 2018, Data Mining and Knowledge Discovery.

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

Eamonn J. Keogh, A. Mueen, Goce Trajcevski, 2010, Data Mining and Knowledge Discovery.

MDL-based time series clustering

Eamonn J. Keogh, S. Lonardi, T. Rakthanmanon, 2012, Knowledge and Information Systems.

CID: an efficient complexity-invariant distance for time series

Eamonn J. Keogh, Gustavo E. A. P. A. Batista, Oben M. Tataw, 2014, Data Mining and Knowledge Discovery.

Introducing the contrast profile: a novel time series primitive that allows real world classification

Eamonn J. Keogh, Ryan Mercer, N. S. Senobari, 2022, Data mining and knowledge discovery.

R Bindings for the UCR Suite [R package rucrdtw version 0.1.4]

Eamonn J. Keogh, Eamonn Keogh, Gustavo E. A. P. A. Batista, 2020 .

Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems

Li Wei, Eamonn J. Keogh, Stefano Lonardi, 2006, Sixth International Conference on Data Mining (ICDM'06).

Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems

Li Wei, Eamonn J. Keogh, Stefano Lonardi, 2005, J. Univers. Comput. Sci..

Efficiently finding unusual shapes in large image databases

Li Wei, Eamonn J. Keogh, Eamonn Keogh, 2008, Data Mining and Knowledge Discovery.

Alcohol burns in the neonate

Eamonn J. Keogh, A M Watkins, E J Keogh, 1992, Journal of paediatrics and child health.

Everything you know about Dynamic Time Warping is Wrong

Eamonn J. Keogh, Chotirat Ann Ratanamahatana, Eamonn Keogh, 2004 .

Semi-supervised time series classification

Li Wei, Eamonn J. Keogh, Eamonn Keogh, 2006, KDD '06.

GnRH analogues in reproductive medicine

Eamonn J. Keogh, R. Jansen, 1991, The Medical journal of Australia.

Exact indexing of dynamic time warping

Eamonn J. Keogh, Chotirat Ann Ratanamahatana, Eamonn Keogh, 2002, Knowledge and Information Systems.

Super‐Efficient Cross‐Correlation (SEC‐C): A Fast Matched Filtering Code Suitable for Desktop Computers

Eamonn J. Keogh, Yan Zhu, Eamonn Keogh, 2018, Seismological Research Letters.

An ultra-fast time series distance measure to allow data mining in more complex real-world deployments

Eamonn J. Keogh, Eamonn Keogh, Anthony Bagnall, 2020, Data Mining and Knowledge Discovery.

Experiencing SAX: a novel symbolic representation of time series

Li Wei, Jessica Lin, Eamonn J. Keogh, 2007, Data Mining and Knowledge Discovery.

Segmenting Time Series: A Survey and Novel Approach

Eamonn J. Keogh, Eamonn Keogh, Michael J. Pazzani, 2002 .

Introducing time series chains: a new primitive for time series data mining

Eamonn J. Keogh, Yan Zhu, Eamonn Keogh, 2019, Knowledge and Information Systems.

Introducing time series chains: a new primitive for time series data mining

Eamonn J. Keogh, D. Nikovski, Yan Zhu, 2018, Knowledge and Information Systems.

Classification of streaming time series under more realistic assumptions

Eamonn J. Keogh, Bing Hu, Yanping Chen, 2016, Data Mining and Knowledge Discovery.

Online Amnestic DTW to allow Real-Time Golden Batch Monitoring

Eamonn J. Keogh, Eamonn Keogh, Hoang Anh Dau, 2019, KDD.

Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures

Pavlos Protopapas, Eamonn J. Keogh, Eamonn Keogh, 2008, The VLDB Journal.

Introducing the contrast profile: a novel time series primitive that allows real world classification

Eamonn J. Keogh, Ryan Mercer, N. S. Senobari, 2022, Data Min. Knowl. Discov..

Parasitic mites alter chicken behaviour and negatively impact animal welfare

Eamonn J. Keogh, A. Gerry, A. Murillo, 2020, Scientific Reports.

Clustering in the Face of Fast Changing Streams

Eamonn J. Keogh, Eamonn Keogh, Nurjahan Begum, 2016, SDM.

Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy

Eamonn J. Keogh, Eamonn Keogh, Nurjahan Begum, 2015, KDD.

Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series

Eamonn J. Keogh, S. Lonardi, T. Rakthanmanon, 2015, Data Mining and Knowledge Discovery.

Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification

Geoffrey I. Webb, Germain Forestier, Ann E. Nicholson, 2014, 2014 IEEE International Conference on Data Mining.

Clustering Time Series Using Unsupervised-Shapelets

Eamonn J. Keogh, J. Zakaria, A. Mueen, 2012, 2012 IEEE 12th International Conference on Data Mining.

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

Eamonn J. Keogh, R. Wu, 2023, IEEE Transactions on Knowledge and Data Engineering.

Flying Insect Classification with Inexpensive Sensors

Eamonn J. Keogh, Eamonn Keogh, Gustavo E. A. P. A. Batista, 2014, Journal of Insect Behavior.

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

Maria A. Zuluaga, Eamonn J. Keogh, A. Mueen, 2022, KDD.

Matrix profile goes MAD: variable-length motif and discord discovery in data series

Eamonn J. Keogh, Themis Palpanas, Yan Zhu, 2020, Data Mining and Knowledge Discovery.

LB_Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures

Eamonn J. Keogh, Eamonn Keogh, Michail Vlachos, 2006, VLDB.

Time series classification for varying length series pdf

Geoffrey I. Webb, Eamonn J. Keogh, Eamonn Keogh, 2019, ArXiv.

Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping

Eamonn J. Keogh, J. Dauwels, J. Jing, 2016, Journal of Neuroscience Methods.

Fast Best-Match Shape Searching in Rotation-Invariant Metric Spaces

Li Wei, Eamonn J. Keogh, Eamonn Keogh, 2007, IEEE Transactions on Multimedia.

Learning from time series in the presence of noise: unsupervised and semi-supervised approaches

Eamonn J. Keogh, Eamonn Keogh, Dragomir Yankov, 2008 .

The use of a new vasoconstrictor in the management of burns.

Eamonn J. Keogh, A. Clarke, J. Solomon, 1973, The Medical journal of Australia.

Matrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness

Eamonn J. Keogh, Eamonn Keogh, Takaaki Nakamura, 2020, KDD.

Multi-Resolution K-Means Clustering of Time Series and Application to Images

Dimitrios Gunopulos, Eamonn J. Keogh, Jessica Lin, 2003 .

Implementation of EQ-5D-5L as a routine outcome measure in Community Outpatient and Specialized Rehabilitation Services

Eamonn J. Keogh, Lisa J Warner, Katie Churchill, 2021, Journal of Patient-Reported Outcomes.

Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile

Eamonn J. Keogh, Hoang Anh Dau, Diego Furtado Silva, 2017, Data Mining and Knowledge Discovery.

Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW pdf

Eamonn J. Keogh, Eamonn Keogh, Kaveh Kamgar, 2020, 2020 IEEE International Conference on Data Mining (ICDM).

Eamonn J. Keogh, Yan Zhu, Eamonn Keogh, 2019, IEEE Transactions on Multimedia.

Fitbit for Chickens?: Time Series Data Mining Can Increase the Productivity of Poultry Farms

Eamonn J. Keogh, Eamonn Keogh, Shima Imani, 2020, KDD.

A Practical Tool for Visualizing and Data Mining Medical Time Series

Li Wei, Eamonn J. Keogh, Stefano Lonardi, 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

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

Eamonn J. Keogh, A. Bagnall, Jason Lines, 2016, Data Mining and Knowledge Discovery.

Dimensionality Reduction

Eamonn J. Keogh, Daniel Leeds, A. Mueen, 2018, Encyclopedia of Machine Learning and Data Mining.

Establishing the provenance of historical manuscripts with a novel distance measure

Eamonn J. Keogh, Bilson J. L. Campana, A. Mueen, 2013, Pattern Analysis and Applications.

A new feature set with new window techniques for customer churn prediction in land-line telecommunications

M. Tahar Kechadi, Eamonn J. Keogh, Tarik A. Rashid, 2010, Expert Syst. Appl..

Facets of self-oriented and socially prescribed perfectionism and feelings of pride, shame, and guilt following success and failure

Eamonn J. Keogh, Joachim Stoeber, Tom Kempe, 2008 .

When is Early Classification of Time Series Meaningful?

Eamonn J. Keogh, R. Wu, Audrey Der, 2023, IEEE Transactions on Knowledge and Data Engineering.

Compression-based data mining of sequential data

Li Wei, John C. Handley, Eamonn J. Keogh, 2007, Data Mining and Knowledge Discovery.

Novelets: a new primitive that allows online detection of emerging behaviors in time series

Eamonn J. Keogh, Ryan Mercer, 2023, Knowledge and Information Systems.

MERLIN++: parameter-free discovery of time series anomalies

Eamonn J. Keogh, Takaaki Nakamura, Makoto Imamura, 2023, Data Mining and Knowledge Discovery.

Matrix Profile XXV: Introducing Novelets: A Primitive that Allows Online Detection of Emerging Behaviors in Time Series

Eamonn J. Keogh, Ryan Mercer, 2022, 2022 IEEE International Conference on Data Mining (ICDM).

Error-bounded Approximate Time Series Joins using Compact Dictionary Representations of Time Series pdf

Eamonn J. Keogh, Eamonn Keogh, Zhongfang Zhuang, 2021, SDM.

FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates

Eamonn J. Keogh, R. Wu, 2022, IEEE Transactions on Knowledge and Data Engineering.

Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping

Eamonn J. Keogh, Gustavo E. A. P. A. Batista, Bilson J. L. Campana, 2012, KDD.

Future Research

Eamonn J. Keogh, Jessica Lin, 2020, Information Theory Meets Power Laws.

Domain-Driven, Actionable Knowledge Discovery

Eamonn J. Keogh, Chengqi Zhang, Longbing Cao, 2007, IEEE Intell. Syst..

SAXually Explicit Images: Finding Unusual Shapes

Li Wei, Eamonn J. Keogh, Eamonn Keogh, 2006, Sixth International Conference on Data Mining (ICDM'06).

Matrix Profile XVII: Indexing the Matrix Profile to Allow Arbitrary Range Queries

Eamonn J. Keogh, Eamonn Keogh, Yan Zhu, 2020, 2020 IEEE 36th International Conference on Data Engineering (ICDE).

Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins

Eamonn J. Keogh, Chin-Chia Michael Yeh, A. Mueen, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).