Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases. - Eamonn J. Keogh (ICTAI, 1997)

A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. - Eamonn J. Keogh; Padhraic Smyth (KDD, 1997)

An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. - Eamonn J. Keogh; Michael J. Pazzani (KDD, 1998)

Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. - Eamonn J. Keogh; Michael J. Pazzani (AISTATS, 1999)

Scaling up Dynamic Time Warping to Massive Dataset. - Eamonn J. Keogh; Michael J. Pazzani (PKDD, 1999)

Relevance Feedback Retrieval of Time Series Data. - Eamonn J. Keogh; Michael J. Pazzani (SIGIR, 1999)

An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. - Eamonn J. Keogh; Michael J. Pazzani (SSDBM, 1999)

Pseudo Periodic Synthetic Time Series. - Eamonn J. Keogh; Michael J. Pazzani (UCI Machine Learning Repository, 1999)

Scaling up dynamic time warping for datamining applications. - Eamonn J. Keogh; Michael J. Pazzani (KDD, 2000)

A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. - Eamonn J. Keogh; Michael J. Pazzani (PAKDD, 2000)

An Online Algorithm for Segmenting Time Series. - Eamonn J. Keogh; Selina Chu; David M. Hart; Michael J. Pazzani (ICDM, 2001)

Ensemble-index: a new approach to indexing large databases. - Eamonn J. Keogh; Selina Chu; Michael J. Pazzani (KDD, 2001)

Derivative Dynamic Time Warping. - Eamonn J. Keogh; Michael J. Pazzani (SDM, 2001)

Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. - Eamonn J. Keogh; Kaushik Chakrabarti; Michael J. Pazzani; Sharad Mehrotra (Knowl. Inf. Syst., 2001)

An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data. - Eamonn J. Keogh; Harry Hochheiser; Ben Shneiderman (FQAS, 2002)

Mining Motifs in Massive Time Series Databases. - Pranav Patel; Eamonn J. Keogh; Jessica Lin 001; Stefano Lonardi (ICDM, 2002)

Finding surprising patterns in a time series database in linear time and space. - Eamonn J. Keogh; Stefano Lonardi; Bill Yuan-chi Chiu (KDD, 2002)

Indexing and Mining Time Series. - Eamonn J. Keogh (SBBD, 2002)

Iterative Deepening Dynamic Time Warping for Time Series. - Selina Chu; Eamonn J. Keogh; David M. Hart; Michael J. Pazzani (SDM, 2002)

Exact Indexing of Dynamic Time Warping. - Eamonn J. Keogh (VLDB, 2002)

Learning the Structure of Augmented Bayesian Classifiers. - Eamonn J. Keogh; Michael J. Pazzani (Int. J. Artif. Intell. Tools, 2002)

Locally adaptive dimensionality reduction for indexing large time series databases. - Kaushik Chakrabarti; Eamonn J. Keogh; Sharad Mehrotra; Michael J. Pazzani (ACM Trans. Database Syst., 2002)

A symbolic representation of time series, with implications for streaming algorithms. - Jessica Lin 001; Eamonn J. Keogh; Stefano Lonardi; Bill Yuan-chi Chiu (DMKD, 2003)

Clustering of streaming time series is meaningless. - Jessica Lin 001; Eamonn J. Keogh; Wagner Truppel (DMKD, 2003)

(Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research. - Jessica Lin 001; Eamonn J. Keogh; Wagner Truppel (IC-AI, 2003)

Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. - Eamonn J. Keogh; Jessica Lin 001; Wagner Truppel (ICDM, 2003)

Grid-Based Indexing for Large Time Series Databases. - Jiyuan An; Hanxiong Chen; Kazutaka Furuse; Nobuo Ohbo; Eamonn J. Keogh (IDEAL, 2003)

Probabilistic discovery of time series motifs. - Bill Yuan-chi Chiu; Eamonn J. Keogh; Stefano Lonardi (KDD, 2003)

Indexing multi-dimensional time-series with support for multiple distance measures. - Michail Vlachos; Marios Hadjieleftheriou; Dimitrios Gunopulos; Eamonn J. Keogh (KDD, 2003)

Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases. - Eamonn J. Keogh (PKDD, 2003)

A Gentle Introduction to Machine Learning and Data Mining for the Database Community. - Eamonn J. Keogh (SBBD, 2003)

On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. - Eamonn J. Keogh; Shruti Kasetty (Data Min. Knowl. Discov., 2003)

We Have Seen the Future, and It Is Symbolic. - Eamonn J. Keogh; Jessica Lin 001; Stefano Lonardi; Bill Yuan-chi Chiu (ACSW, 2004)

Iterative Incremental Clustering of Time Series. - Jessica Lin 001; Michail Vlachos; Eamonn J. Keogh; Dimitrios Gunopulos (EDBT, 2004)

Online Amnesic Approximation of Streaming Time Series. - Themistoklis Palpanas; Michail Vlachos; Eamonn J. Keogh; Dimitrios Gunopulos; Wagner Truppel (ICDE, 2004)

Towards parameter-free data mining. - Eamonn J. Keogh; Stefano Lonardi; Chotirat (Ann) Ratanamahatana (KDD, 2004)

Visually mining and monitoring massive time series. - Jessica Lin 001; Eamonn J. Keogh; Stefano Lonardi; Jeffrey P. Lankford; Donna M. Nystrom (KDD, 2004)

Making Time-Series Classification More Accurate Using Learned Constraints. - Chotirat (Ann) Ratanamahatana; Eamonn J. Keogh (SDM, 2004)

VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases. - Jessica Lin 001; Eamonn J. Keogh; Stefano Lonardi; Jeffrey P. Lankford; Donna M. Nystrom (VLDB, 2004)

Indexing Large Human-Motion Databases. - Eamonn J. Keogh; Themis Palpanas; Victor B. Zordan; Dimitrios Gunopulos; Marc Cardle (VLDB, 2004)

A Grid-Based Index Method for Time Warping Distance. - Jiyuan An; Yi-Ping Phoebe Chen; Eamonn J. Keogh (WAIM, 2004)

Indexing Multi-Dimensional Trajectories for Similarity Queries. - Michail Vlachos; Marios Hadjieleftheriou; Eamonn J. Keogh; Dimitrios Gunopulos (Spatial Databases, 2005)

Mining Time Series Data. - Chotirat (Ann) Ratanamahatana; Jessica Lin 001; Dimitrios Gunopulos; Eamonn J. Keogh; Michail Vlachos; Gautam Das 001 (The Data Mining and Knowledge Discovery Handbook, 2005)

Approximations to Magic: Finding Unusual Medical Time Series. - Jessica Lin 001; Eamonn J. Keogh; Ada Wai-Chee Fu; Helga Van Herle (CBMS, 2005)

A Practical Tool for Visualizing and Data Mining Medical Time Series. - Li Wei 001; Nitin Kumar 002; Venkata Nishanth Lolla; Eamonn J. Keogh; Stefano Lonardi; Chotirat (Ann) Ratanamahatana; Helga Van Herle (CBMS, 2005)

Multimedia Retrieval Using Time Series Representation and Relevance Feedback. - Chotirat (Ann) Ratanamahatana; Eamonn J. Keogh (ICADL, 2005)

HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. - Eamonn J. Keogh; Jessica Lin 001; Ada Wai-Chee Fu (ICDM, 2005)

Partial Elastic Matching of Time Series. - Longin Jan Latecki; Vasileios Megalooikonomou; Qiang Wang 010; Rolf Lakämper; Chotirat (Ann) Ratanamahatana; Eamonn J. Keogh (ICDM, 2005)

Atomic Wedgie: Efficient Query Filtering for Streaming Times Series. - Li Wei 001; Eamonn J. Keogh; Helga Van Herle; Agenor Mafra-Neto (ICDM, 2005)

Dot Plots for Time Series Analysis. - Dragomir Yankov; Eamonn J. Keogh; Stefano Lonardi; Ada Wai-Chee Fu (ICTAI, 2005)

Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases. - Chotirat (Ann) Ratanamahatana; Eamonn J. Keogh (KES (2), 2005)

Efficient trajectory joins using symbolic representations. - Petko Bakalov; Marios Hadjieleftheriou; Eamonn J. Keogh; Vassilis J. Tsotras (Mobile Data Management, 2005)

A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams. - Jessica Lin 001; Michail Vlachos; Eamonn J. Keogh; Dimitrios Gunopulos; Jian-Wei Liu; Shou-Jian Yu; Jia-Jin Le (PAKDD, 2005)

A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering. - Chotirat (Ann) Ratanamahatana; Eamonn J. Keogh; Anthony J. Bagnall; Stefano Lonardi (PAKDD, 2005)

Recent Advances in Mining Time Series Data. - Eamonn J. Keogh (PKDD, 2005)

Elastic Partial Matching of Time Series. - Longin Jan Latecki; Vasilis Megalooikonomou; Qiang Wang 010; Rolf Lakämper; Chotirat (Ann) Ratanamahatana; Eamonn J. Keogh (PKDD, 2005)

Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases. - Nitin Kumar 002; Venkata Nishanth Lolla; Eamonn J. Keogh; Stefano Lonardi; Chotirat (Ann) Ratanamahatana (SDM, 2005)

Three Myths about Dynamic Time Warping Data Mining. - Chotirat (Ann) Ratanamahatana; Eamonn J. Keogh (SDM, 2005)

Assumption-Free Anomaly Detection in Time Series. - Li Wei 001; Nitin Kumar 002; Venkata Nishanth Lolla; Eamonn J. Keogh; Stefano Lonardi; Chotirat (Ann) Ratanamahatana (SSDBM, 2005)

Visualization and Mining of Temporal Data. - Eamonn J. Keogh (IEEE Visualization, 2005)

Scaling and Time Warping in Time Series Querying. - Ada Wai-Chee Fu; Eamonn J. Keogh; Leo Yung Hang Lau; Chotirat (Ann) Ratanamahatana (VLDB, 2005)

Visualizing and discovering non-trivial patterns in large time series databases. - Jessica Lin 001; Eamonn J. Keogh; Stefano Lonardi (Inf. Vis., 2005)

Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems. - Li Wei 001; Eamonn J. Keogh; Xiaopeng Xi; Stefano Lonardi (J. Univers. Comput. Sci., 2005)

Clustering of time-series subsequences is meaningless: implications for previous and future research. - Eamonn J. Keogh; Jessica Lin 001 (Knowl. Inf. Syst., 2005)

Exact indexing of dynamic time warping. - Eamonn J. Keogh; Chotirat (Ann) Ratanamahatana (Knowl. Inf. Syst., 2005)

Guest Editorial. - Eamonn J. Keogh (Mach. Learn., 2005)

Finding Time Series Discords Based on Haar Transform. - Ada Wai-Chee Fu; Oscar Tat-Wing Leung; Eamonn J. Keogh; Jessica Lin 001 (ADMA, 2006)

Configurable cache subsetting for fast cache tuning. - Pablo Viana; Ann Gordon-Ross; Eamonn J. Keogh; Edna Barros; Frank Vahid (DAC, 2006)

Ensembles of Nearest Neighbor Forecasts. - Dragomir Yankov; Dennis DeCoste; Eamonn J. Keogh (ECML, 2006)

Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems. - Eamonn J. Keogh; Li Wei 001; Xiaopeng Xi; Stefano Lonardi; Jin Shieh; Scott Sirowy (ICDM, 2006)

Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining. - Ken Ueno; Xiaopeng Xi; Eamonn J. Keogh; Dah-Jye Lee (ICDM, 2006)

Clustering Workflow Requirements Using Compression Dissimilarity Measure. - Li Wei 001; John C. Handley; Nathaniel Martin; Tong Sun; Eamonn J. Keogh (ICDM Workshops, 2006)

SAXually Explicit Images: Finding Unusual Shapes. - Li Wei 001; Eamonn J. Keogh; Xiaopeng Xi (ICDM, 2006)

Manifold Clustering of Shapes. - Dragomir Yankov; Eamonn J. Keogh (ICDM, 2006)

Fast time series classification using numerosity reduction. - Xiaopeng Xi; Eamonn J. Keogh; Christian R. Shelton; Li Wei 001; Chotirat Ann Ratanamahatana (ICML, 2006)

Global distance-based segmentation of trajectories. - Aris Anagnostopoulos; Michail Vlachos; Marios Hadjieleftheriou; Eamonn J. Keogh; Philip S. Yu (KDD, 2006)

Semi-supervised time series classification. - Li Wei 001; Eamonn J. Keogh (KDD, 2006)

Data mining and information retrieval in time series/multimedia databases. - Eamonn J. Keogh (ACM Multimedia, 2006)

Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data. - Jessica Lin 001; Eamonn J. Keogh (PKDD, 2006)

A Decade of Progress in Indexing and Mining Large Time Series Databases. - Eamonn J. Keogh (VLDB, 2006)

LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures. - Eamonn J. Keogh; Li Wei 001; Xiaopeng Xi; Sang-Hee Lee; Michail Vlachos (VLDB, 2006)

A Bit Level Representation for Time Series Data Mining with Shape Based Similarity. - Anthony J. Bagnall; Chotirat (Ann) Ratanamahatana; Eamonn J. Keogh; Stefano Lonardi; Gareth J. Janacek (Data Min. Knowl. Discov., 2006)

Efficient Discovery of Unusual Patterns in Time Series. - Stefano Lonardi; Jessica Lin 001; Eamonn J. Keogh; Bill Yuan-chi Chiu (New Gener. Comput., 2006)

Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications. - Eamonn J. Keogh; Jessica Lin 001; Ada Wai-Chee Fu; Helga Van Herle (IEEE Trans. Inf. Technol. Biomed., 2006)

Indexing Multidimensional Time-Series. - Michail Vlachos; Marios Hadjieleftheriou; Dimitrios Gunopulos; Eamonn J. Keogh (VLDB J., 2006)

TS2-tree - an efficient similarity based organization for trajectory data. - Petko Bakalov; Eamonn J. Keogh; Vassilis J. Tsotras (GIS, 2007)

Locally Constrained Support Vector Clustering. - Dragomir Yankov; Eamonn J. Keogh; Kin Fai Kan (ICDM, 2007)

Detecting time series motifs under uniform scaling. - Dragomir Yankov; Eamonn J. Keogh; Jose Medina; Bill Yuan-chi Chiu; Victor B. Zordan (KDD, 2007)

Visual Exploration of Genomic Data. - Michail Vlachos; Bahar Taneri; Eamonn J. Keogh; Philip S. Yu (PKDD, 2007)

WAT: Finding Top-K Discords in Time Series Database. - Yingyi Bu; Oscar Tat-Wing Leung; Ada Wai-Chee Fu; Eamonn J. Keogh; Jian Pei; Sam Meshkin (SDM, 2007)

Finding Motifs in a Database of Shapes. - Xiaopeng Xi; Eamonn J. Keogh; Li Wei 001; Agenor Mafra-Neto (SDM, 2007)

Compression-based data mining of sequential data. - Eamonn J. Keogh; Stefano Lonardi; Chotirat Ann Ratanamahatana; Li Wei 001; Sang-Hee Lee; John C. Handley (Data Min. Knowl. Discov., 2007)

Experiencing SAX: a novel symbolic representation of time series. - Jessica Lin 001; Eamonn J. Keogh; Li Wei 001; Stefano Lonardi (Data Min. Knowl. Discov., 2007)

Domain-Driven, Actionable Knowledge Discovery. - Longbing Cao; Chengqi Zhang; Qiang Yang 001; David A. Bell; Michail Vlachos; Bahar Taneri; Eamonn J. Keogh; Philip S. Yu; Ning Zhong 001; Mafruz Zaman Ashrafi; David Taniar; Eugene Dubossarsky; et al. (IEEE Intell. Syst., 2007)

Finding the most unusual time series subsequence: algorithms and applications. - Eamonn J. Keogh; Jessica Lin 001; Sang-Hee Lee; Helga Van Herle (Knowl. Inf. Syst., 2007)

Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiers. - Li Wei 001; Eamonn J. Keogh; Helga Van Herle; Agenor Mafra-Neto; Russell J. Abbott (Knowl. Inf. Syst., 2007)

Real-Time Classification of Streaming Sensor Data. - Shashwati Kasetty; Candice Stafford; Gregory P. Walker; Xiaoyue Wang 004; Eamonn J. Keogh (ICTAI (1), 2008)

iSAX: indexing and mining terabyte sized time series. - Jin Shieh; Eamonn J. Keogh (KDD, 2008)

The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining. - Lexiang Ye; Xiaoyue Wang 004; Dragomir Yankov; Eamonn J. Keogh (SDM, 2008)

Efficiently finding unusual shapes in large image databases. - Li Wei 001; Eamonn J. Keogh; Xiaopeng Xi; Melissa Yoder (Data Min. Knowl. Discov., 2008)

Disk aware discord discovery: finding unusual time series in terabyte sized datasets. - Dragomir Yankov; Eamonn J. Keogh; Umaa Rebbapragada (Knowl. Inf. Syst., 2008)

Converting non-parametric distance-based classification to anytime algorithms. - Xiaopeng Xi; Ken Ueno; Eamonn J. Keogh; Dah-Jye Lee (Pattern Anal. Appl., 2008)

Querying and mining of time series data: experimental comparison of representations and distance measures. - Hui Ding 004; Goce Trajcevski; Peter Scheuermann; Xiaoyue Wang 004; Eamonn J. Keogh (Proc. VLDB Endow., 2008)

Streaming Time Series Summarization Using User-Defined Amnesic Functions. - Themis Palpanas; Michail Vlachos; Eamonn J. Keogh; Dimitrios Gunopulos (IEEE Trans. Knowl. Data Eng., 2008)

Fast Best-Match Shape Searching in Rotation-Invariant Metric Spaces. - Dragomir Yankov; Eamonn J. Keogh; Li Wei 001; Xiaopeng Xi; Wendy L. Hodges (IEEE Trans. Multim., 2008)

Scaling and time warping in time series querying. - Ada Wai-Chee Fu; Eamonn J. Keogh; Leo Yung Hang Lau; Chotirat (Ann) Ratanamahatana; Raymond Chi-Wing Wong (VLDB J., 2008)

Indexing and Mining Time Series Data. - Eamonn J. Keogh (Encyclopedia of GIS, 2008)

A Clustering Analysis for Target Group Identification by Locality in Motor Insurance Industry. - Xiaozhe Wang; Eamonn J. Keogh (Soft Computing Applications in Business, 2008)

Finding Time Series Motifs in Disk-Resident Data. - Abdullah Mueen; Eamonn J. Keogh; Nima Bigdely Shamlo (ICDM, 2009)

Augmenting Historical Manuscripts with Automatic Hyperlinks. - Xiaoyue Wang 004; Eamonn J. Keogh (ISM, 2009)

Finding centuries-old hyperlinks with a novel semi-supervised learning technique. - Xiaoyue Wang 004; Eamonn J. Keogh (JCDL, 2009)

Time series shapelets: a new primitive for data mining. - Lexiang Ye; Eamonn J. Keogh (KDD, 2009)

Augmenting the generalized hough transform to enable the mining of petroglyphs. - Qiang Zhu 002; Xiaoyue Wang 004; Eamonn J. Keogh; Sang-Hee Lee (KDD, 2009)

Exact Discovery of Time Series Motifs. - Abdullah Mueen; Eamonn J. Keogh; Qiang Zhu 002; Sydney Cash; M. Brandon Westover (SDM, 2009)

Autocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data Mining. - Lexiang Ye; Xiaoyue Wang 004; Eamonn J. Keogh; Agenor Mafra-Neto (SDM, 2009)

iSAX: disk-aware mining and indexing of massive time series datasets. - Jin Shieh; Eamonn J. Keogh (Data Min. Knowl. Discov., 2009)

Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures. - Eamonn J. Keogh; Li Wei 001; Xiaopeng Xi; Michail Vlachos; Sang-Hee Lee; Pavlos Protopapas (VLDB J., 2009)

Compression-Based Data Mining. - Eamonn J. Keogh; Li Keogh; John C. Handley (Encyclopedia of Data Warehousing and Mining, 2009)

iSAX 2.0: Indexing and Mining One Billion Time Series. - Alessandro Camerra; Themis Palpanas; Jin Shieh; Eamonn J. Keogh (ICDM, 2010)

How to Do Good Data Mining Research and Get it Published in Top Venues. - Eamonn J. Keogh (ICDM, 2010)

Data Editing Techniques to Allow the Application of Distance-Based Outlier Detection to Streams. - Vit Niennattrakul; Eamonn J. Keogh; Chotirat Ann Ratanamahatana (ICDM, 2010)

Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs. - Doruk Sart; Abdullah Mueen; Walid A. Najjar; Eamonn J. Keogh; Vit Niennattrakul (ICDM, 2010)

Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times. - Jin Shieh; Eamonn J. Keogh (ICDM, 2010)

Mother Fugger: Mining Historical Manuscripts with Local Color Patches. - Qiang Zhu 002; Eamonn J. Keogh (ICDM, 2010)

Classification of Live Moths Combining Texture, Color and Shape Primitives. - Gustavo E. A. P. A. Batista; Bilson J. L. Campana; Eamonn J. Keogh (ICMLA, 2010)

Using CAPTCHAs to Index Cultural Artifacts. - Qiang Zhu 002; Eamonn J. Keogh (IDA, 2010)

Online discovery and maintenance of time series motifs. - Abdullah Mueen; Eamonn J. Keogh (KDD, 2010)

Experimental Comparison of Representation Methods and Distance Measures for Time Series Data - Xiaoyue Wang 004; Hui Ding 004; Goce Trajcevski; Peter Scheuermann; Eamonn J. Keogh (CoRR, 2010)

Annotating Historical Archives of Images. - Xiaoyue Wang 004; Lexiang Ye; Eamonn J. Keogh; Christian R. Shelton (Int. J. Digit. Libr. Syst., 2010)

A compression-based distance measure for texture. - Bilson J. L. Campana; Eamonn J. Keogh (Stat. Anal. Data Min., 2010)

A brief survey on sequence classification. - Zhengzheng Xing; Jian Pei; Eamonn J. Keogh (SIGKDD Explor., 2010)

Mining Time Series Data. - Chotirat Ann Ratanamahatana; Jessica Lin 001; Dimitrios Gunopulos; Eamonn J. Keogh; Michail Vlachos; Gautam Das 001 (Data Mining and Knowledge Discovery Handbook, 2010)

Instance-Based Learning. - Eamonn J. Keogh (Encyclopedia of Machine Learning, 2010)

Nearest Neighbor. - Eamonn J. Keogh (Encyclopedia of Machine Learning, 2010)

Time Series. - Eamonn J. Keogh (Encyclopedia of Machine Learning, 2010)

Curse of Dimensionality. - Eamonn J. Keogh; Abdullah Mueen (Encyclopedia of Machine Learning, 2010)

Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL. - Bing Hu 001; Thanawin Rakthanmanon; Yuan Hao; Scott Evans; Stefano Lonardi; Eamonn J. Keogh (Algorithmic Probability and Friends, 2011)

Searching historical manuscripts for near-duplicate figures. - Thanawin Rakthanmanon; Qiang Zhu 002; Eamonn J. Keogh (HIP@ICDAR, 2011)

Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL. - Bing Hu 001; Thanawin Rakthanmanon; Yuan Hao; Scott Evans; Stefano Lonardi; Eamonn J. Keogh (ICDM, 2011)

Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data. - Thanawin Rakthanmanon; Eamonn J. Keogh; Stefano Lonardi; Scott Evans (ICDM, 2011)

Mining Historical Documents for Near-Duplicate Figures. - Thanawin Rakthanmanon; Qiang Zhu 002; Eamonn J. Keogh (ICDM, 2011)

Towards Automatic Classification on Flying Insects Using Inexpensive Sensors. - Gustavo E. A. P. A. Batista; Yuan Hao; Eamonn J. Keogh; Agenor Mafra-Neto (ICMLA (1), 2011)

SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining. - Gustavo E. A. P. A. Batista; Eamonn J. Keogh; Agenor Mafra-Neto; Edgar Rowton (KDD, 2011)

Logical-shapelets: an expressive primitive for time series classification. - Abdullah Mueen; Eamonn J. Keogh; Neal E. Young (KDD, 2011)

A Complexity-Invariant Distance Measure for Time Series. - Gustavo E. A. P. A. Batista; Xiaoyue Wang 004; Eamonn J. Keogh (SDM, 2011)

A disk-aware algorithm for time series motif discovery. - Abdullah Mueen; Eamonn J. Keogh; Qiang Zhu 002; Sydney Cash; M. Brandon Westover; Nima Bigdely Shamlo (Data Min. Knowl. Discov., 2011)

Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. - Lexiang Ye; Eamonn J. Keogh (Data Min. Knowl. Discov., 2011)

An efficient and effective similarity measure to enable data mining of petroglyphs. - Qiang Zhu 002; Xiaoyue Wang 004; Eamonn J. Keogh; Sang-Hee Lee (Data Min. Knowl. Discov., 2011)

Data, Mining Time Series Data. - Eamonn J. Keogh (International Encyclopedia of Statistical Science, 2011)

Diversifying query results on semi-structured data. - Mahbub Hasan; Abdullah Mueen; Vassilis J. Tsotras; Eamonn J. Keogh (CIKM, 2012)

Clustering Time Series Using Unsupervised-Shapelets. - Jesin Zakaria; Abdullah Mueen; Eamonn J. Keogh (ICDM, 2012)

Searching and mining trillions of time series subsequences under dynamic time warping. - Thanawin Rakthanmanon; Bilson J. L. Campana; Abdullah Mueen; Gustavo E. A. P. A. Batista; M. Brandon Westover; Qiang Zhu 002; Jesin Zakaria; Eamonn J. Keogh (KDD, 2012)

Monitoring and Mining Insect Sounds in Visual Space. - Yuan Hao; Bilson J. L. Campana; Eamonn J. Keogh (SDM, 2012)

Image Mining of Historical Manuscripts to Establish Provenance. - Bing Hu 001; Thanawin Rakthanmanon; Bilson J. L. Campana; Abdullah Mueen; Eamonn J. Keogh (SDM, 2012)

Mining Massive Archives of Mice Sounds with Symbolized Representations. - Jesin Zakaria; Sarah Rotschafer; Abdullah Mueen; Khaleel Razak; Eamonn J. Keogh (SDM, 2012)

A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets. - Qiang Zhu 002; Gustavo E. A. P. A. Batista; Thanawin Rakthanmanon; Eamonn J. Keogh (SDM, 2012)

Getting your acceptance rate to 80%: a checklist for publishing. - Eamonn J. Keogh (SIGMOD/PODS PhD Symposium, 2012)

Efficiently Finding Near Duplicate Figures in Archives of Historical Documents. - Thanawin Rakthanmanon; Qiang Zhu 002; Eamonn J. Keogh (J. Multim., 2012)

MDL-based time series clustering. - Thanawin Rakthanmanon; Eamonn J. Keogh; Stefano Lonardi; Scott Evans (Knowl. Inf. Syst., 2012)

Mining historical manuscripts with local color patches. - Qiang Zhu 002; Eamonn J. Keogh (Knowl. Inf. Syst., 2012)

Instruction set extensions for Dynamic Time Warping. - Joseph Tarango; Eamonn J. Keogh; Philip Brisk (CODES+ISSS, 2013)

Clustering of Symbols Using Minimal Description Length. - Oben M. Tataw; Thanawin Rakthanmanon; Eamonn J. Keogh (ICDAR, 2013)

Classification of Multi-dimensional Streaming Time Series by Weighting Each Classifier's Track Record. - Bing Hu 001; Yanping Chen 005; Jesin Zakaria; Liudmila Ulanova; Eamonn J. Keogh (ICDM, 2013)

Parameter-Free Audio Motif Discovery in Large Data Archives. - Yuan Hao; Mohammad Shokoohi-Yekta; George Papageorgiou; Eamonn J. Keogh (ICDM, 2013)

Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps. - Diego Furtado Silva; Vinícius M. A. de Souza; Gustavo E. A. P. A. Batista; Eamonn J. Keogh; Daniel P. W. Ellis (ICMLA (1), 2013)

Data Mining a Trillion Time Series Subsequences Under Dynamic Time Warping. - Thanawin Rakthanmanon; Eamonn J. Keogh (IJCAI, 2013)

Towards a minimum description length based stopping criterion for semi-supervised time series classification. - Nurjahan Begum; Bing Hu 001; Thanawin Rakthanmanon; Eamonn J. Keogh (IRI, 2013)

A Minimum Description Length Technique for Semi-Supervised Time Series Classification. - Nurjahan Begum; Bing Hu 001; Thanawin Rakthanmanon; Eamonn J. Keogh (IRI (best papers), 2013)

DTW-D: time series semi-supervised learning from a single example. - Yanping Chen 005; Bing Hu 001; Eamonn J. Keogh; Gustavo E. A. P. A. Batista (KDD, 2013)

Towards never-ending learning from time series streams. - Yuan Hao; Yanping Chen 005; Jesin Zakaria; Bing Hu 001; Thanawin Rakthanmanon; Eamonn J. Keogh (KDD, 2013)

Time Series Classification under More Realistic Assumptions. - Bing Hu 001; Yanping Chen 005; Eamonn J. Keogh (SDM, 2013)

Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets. - Eamonn J. Keogh; Thanawin Rakthanmanon (SDM, 2013)

Experimental comparison of representation methods and distance measures for time series data. - Xiaoyue Wang 004; Abdullah Mueen; Hui Ding 004; Goce Trajcevski; Peter Scheuermann; Eamonn J. Keogh (Data Min. Knowl. Discov., 2013)

Quantitative Analysis of Live-Cell Growth at the Shoot Apex of Arabidopsis thaliana: Algorithms for Feature Measurement and Temporal Alignment. - Oben M. Tataw; G. Venugopala Reddy; Eamonn J. Keogh; Amit K. Roy-Chowdhury (IEEE ACM Trans. Comput. Biol. Bioinform., 2013)

Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping. - Thanawin Rakthanmanon; Bilson J. L. Campana; Abdullah Mueen; Gustavo E. A. P. A. Batista; M. Brandon Westover; Qiang Zhu 002; Jesin Zakaria; Eamonn J. Keogh (ACM Trans. Knowl. Discov. Data, 2013)

Accelerating the dynamic time warping distance measure using logarithmetic arithmetic. - Joseph Tarango; Eamonn J. Keogh; Philip Brisk (ACSSC, 2014)

Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification. - François Petitjean; Germain Forestier; Geoffrey I. Webb; Ann E. Nicholson; Yanping Chen 005; Eamonn J. Keogh (ICDM, 2014)

Generating Synthetic Data to Allow Learning from a Single Exemplar per Class. - Liudmila Ulanova; Yuan Hao; Eamonn J. Keogh (SISAP, 2014)

Flying Insect Classification with Inexpensive Sensors. - Yanping Chen 005; Adena Why; Gustavo E. A. P. A. Batista; Agenor Mafra-Neto; Eamonn J. Keogh (CoRR, 2014)

CID: an efficient complexity-invariant distance for time series. - Gustavo E. A. P. A. Batista; Eamonn J. Keogh; Oben Moses Tataw; Vinícius M. A. de Souza (Data Min. Knowl. Discov., 2014)

Beyond one billion time series: indexing and mining very large time series collections with i SAX2+. - Alessandro Camerra; Jin Shieh; Themis Palpanas; Thanawin Rakthanmanon; Eamonn J. Keogh (Knowl. Inf. Syst., 2014)

Rare Time Series Motif Discovery from Unbounded Streams. - Nurjahan Begum; Eamonn J. Keogh (Proc. VLDB Endow., 2014)

Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy. - Nurjahan Begum; Liudmila Ulanova; Jun Wang 037; Eamonn J. Keogh (KDD, 2015)

Discovery of Meaningful Rules in Time Series. - Mohammad Shokoohi-Yekta; Yanping Chen 005; Bilson J. L. Campana; Bing Hu 001; Jesin Zakaria; Eamonn J. Keogh (KDD, 2015)

Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems. - Liudmila Ulanova; Tan Yan; Haifeng Chen; Guofei Jiang; Eamonn J. Keogh; Kai Zhang 001 (KDD, 2015)

On the Non-Trivial Generalization of Dynamic Time Warping to the Multi-Dimensional Case. - Mohammad Shokoohi-Yekta; Jun Wang 037; Eamonn J. Keogh (SDM, 2015)

Scalable Clustering of Time Series with U-Shapelets. - Liudmila Ulanova; Nurjahan Begum; Eamonn J. Keogh (SDM, 2015)

Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series. - Bing Hu 001; Thanawin Rakthanmanon; Yuan Hao; Scott Evans; Stefano Lonardi; Eamonn J. Keogh (Data Min. Knowl. Discov., 2015)

A general framework for never-ending learning from time series streams. - Yanping Chen 005; Yuan Hao; Thanawin Rakthanmanon; Jesin Zakaria; Bing Hu 001; Eamonn J. Keogh (Data Min. Knowl. Discov., 2015)

Exploring Low Cost Laser Sensors to Identify Flying Insect Species - Evaluation of Machine Learning and Signal Processing Methods. - Diego Furtado Silva; Vinícius M. A. de Souza; Daniel P. W. Ellis; Eamonn J. Keogh; Gustavo E. A. P. A. Batista (J. Intell. Robotic Syst., 2015)

Establishing the provenance of historical manuscripts with a novel distance measure. - Bing Hu 001; Thanawin Rakthanmanon; Bilson J. L. Campana; Abdullah Mueen; Eamonn J. Keogh (Pattern Anal. Appl., 2015)

Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping. - Hoang Anh Dau; Nurjahan Begum; Eamonn J. Keogh (CIKM, 2016)

Prefix and Suffix Invariant Dynamic Time Warping. - Diego Furtado Silva; Gustavo E. A. P. A. Batista; Eamonn J. Keogh (ICDM, 2016)

Matrix Profile III: The Matrix Profile Allows Visualization of Salient Subsequences in Massive Time Series. - Chin-Chia Michael Yeh; Helga Van Herle; Eamonn J. Keogh (ICDM, 2016)

Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets. - Chin-Chia Michael Yeh; Yan Zhu 014; Liudmila Ulanova; Nurjahan Begum; Yifei Ding; Hoang Anh Dau; Diego Furtado Silva; Abdullah Mueen; Eamonn J. Keogh (ICDM, 2016)

Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins. - Yan Zhu 014; Zachary Zimmerman; Nader Shakibay Senobari; Chin-Chia Michael Yeh; Gareth J. Funning; Abdullah Mueen; Philip Brisk; Eamonn J. Keogh (ICDM, 2016)

SiMPle: Assessing Music Similarity Using Subsequences Joins. - Diego Furtado Silva; Chin-Chia Michael Yeh; Gustavo E. A. P. A. Batista; Eamonn J. Keogh (ISMIR, 2016)

Extracting Optimal Performance from Dynamic Time Warping. - Abdullah Mueen; Eamonn J. Keogh (KDD, 2016)

Clustering in the Face of Fast Changing Streams. - Liudmila Ulanova; Nurjahan Begum; Mohammad Shokoohi-Yekta; Eamonn J. Keogh (SDM, 2016)

A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy. - Nurjahan Begum; Liudmila Ulanova; Hoang Anh Dau; Jun Wang 037; Eamonn J. Keogh (CoRR, 2016)

Classification of streaming time series under more realistic assumptions. - Bing Hu 001; Yanping Chen 005; Eamonn J. Keogh (Data Min. Knowl. Discov., 2016)

Accelerating the discovery of unsupervised-shapelets. - Jesin Zakaria; Abdullah Mueen; Eamonn J. Keogh; Neal E. Young (Data Min. Knowl. Discov., 2016)

Irrevocable-choice algorithms for sampling from a stream. - Yan Zhu 014; Eamonn J. Keogh (Data Min. Knowl. Discov., 2016)

Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm. - François Petitjean; Germain Forestier; Geoffrey I. Webb; Ann E. Nicholson; Yanping Chen 005; Eamonn J. Keogh (Knowl. Inf. Syst., 2016)

Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series. - Hoang Anh Dau; Diego Furtado Silva; François Petitjean; Germain Forestier; Anthony J. Bagnall; Eamonn J. Keogh (IEEE BigData, 2017)

Searching Time Series with Invariance to Large Amounts of Uniform Scaling. - Yilin Shen; Yanping Chen 005; Eamonn J. Keogh; Hongxia Jin (ICDE, 2017)

Generating Synthetic Time Series to Augment Sparse Datasets. - Germain Forestier; François Petitjean; Hoang Anh Dau; Geoffrey I. Webb; Eamonn J. Keogh (ICDM, 2017)

Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels. - Shaghayegh Gharghabi; Yifei Ding; Chin-Chia Michael Yeh; Kaveh Kamgar; Liudmila Ulanova; Eamonn J. Keogh (ICDM, 2017)

Matrix Profile VI: Meaningful Multidimensional Motif Discovery. - Chin-Chia Michael Yeh; Nickolas Kavantzas; Eamonn J. Keogh (ICDM, 2017)

Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining (Best Student Paper Award). - Yan Zhu 014; Makoto Imamura; Daniel Nikovski; Eamonn J. Keogh (ICDM, 2017)

Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery. - Hoang Anh Dau; Eamonn J. Keogh (KDD, 2017)

Query Suggestion to allow Intuitive Interactive Search in Multidimensional Time Series. - Yifei Ding; Eamonn J. Keogh (SSDBM, 2017)

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. - Anthony J. Bagnall; Jason Lines; Aaron Bostrom; James Large; Eamonn J. Keogh (Data Min. Knowl. Discov., 2017)

Reliable early classification of time series based on discriminating the classes over time. - Usue Mori; Alexander Mendiburu; Eamonn J. Keogh; José Antonio Lozano 001 (Data Min. Knowl. Discov., 2017)

Generalizing DTW to the multi-dimensional case requires an adaptive approach. - Mohammad Shokoohi-Yekta; Bing Hu 001; Hongxia Jin; Jun Wang 037; Eamonn J. Keogh (Data Min. Knowl. Discov., 2017)

Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes. - Chin-Chia Michael Yeh; Nickolas Kavantzas; Eamonn J. Keogh (Proc. VLDB Endow., 2017)

Indexing and Mining Time Series Data. - Eamonn J. Keogh (Encyclopedia of GIS, 2017)

Instance-Based Learning. - Eamonn J. Keogh (Encyclopedia of Machine Learning and Data Mining, 2017)

Nearest Neighbor. - Eamonn J. Keogh (Encyclopedia of Machine Learning and Data Mining, 2017)

Time Series. - Eamonn J. Keogh (Encyclopedia of Machine Learning and Data Mining, 2017)

Curse of Dimensionality. - Eamonn J. Keogh; Abdullah Mueen (Encyclopedia of Machine Learning and Data Mining, 2017)

Matrix Profile XIII: Time Series Snippets: A New Primitive for Time Series Data Mining. - Shima Imani; Frank Madrid; Wei Ding 003; Scott E. Crouter; Eamonn J. Keogh (ICBK, 2018)

Generalized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances. - Rodica Neamtu; Ramoza Ahsan; Elke A. Rundensteiner; Gábor N. Sárközy; Eamonn J. Keogh; Hoang Anh Dau; Cuong Nguyen; Charles Lovering (ICDE, 2018)

Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds. - Yan Zhu 014; Chin-Chia Michael Yeh; Zachary Zimmerman; Kaveh Kamgar; Eamonn J. Keogh (ICDM, 2018)

Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios. - Shaghayegh Gharghabi; Shima Imani; Anthony J. Bagnall; Amirali Darvishzadeh; Eamonn J. Keogh (ICDM, 2018)

Time Series Classification to Improve Poultry Welfare. - Alireza Abdoli; Amy C. Murillo; Chin-Chia Michael Yeh; Alec C. Gerry; Eamonn J. Keogh (ICMLA, 2018)

Time Series Chains: A Novel Tool for Time Series Data Mining. - Yan Zhu 014; Makoto Imamura; Daniel Nikovski; Eamonn J. Keogh (IJCAI, 2018)

Accelerating Time Series Searching with Large Uniform Scaling. - Yilin Shen; Yanping Chen 005; Eamonn J. Keogh; Hongxia Jin (SDM, 2018)

Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series. - Michele Linardi; Yan Zhu 014; Themis Palpanas; Eamonn J. Keogh (SIGMOD Conference, 2018)

VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series. - Michele Linardi; Yan Zhu 014; Themis Palpanas; Eamonn J. Keogh (SIGMOD Conference, 2018)

Admissible Time Series Motif Discovery with Missing Data. - Yan Zhu 014; Abdullah Mueen; Eamonn J. Keogh (CoRR, 2018)

The UEA multivariate time series classification archive, 2018. - Anthony J. Bagnall; Hoang Anh Dau; Jason Lines; Michael Flynn; James Large; Aaron Bostrom; Paul Southam; Eamonn J. Keogh (CoRR, 2018)

Representation Learning by Reconstructing Neighborhoods. - Chin-Chia Michael Yeh; Yan Zhu 014; Evangelos E. Papalexakis; Abdullah Mueen; Eamonn J. Keogh (CoRR, 2018)

Optimizing dynamic time warping's window width for time series data mining applications. - Hoang Anh Dau; Diego Furtado Silva; François Petitjean; Germain Forestier; Anthony J. Bagnall; Abdullah Mueen; Eamonn J. Keogh (Data Min. Knowl. Discov., 2018)

Speeding up similarity search under dynamic time warping by pruning unpromising alignments. - Diego Furtado Silva; Rafael Giusti; Eamonn J. Keogh; Gustavo E. A. P. A. Batista (Data Min. Knowl. Discov., 2018)

Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile. - Chin-Chia Michael Yeh; Yan Zhu 014; Liudmila Ulanova; Nurjahan Begum; Yifei Ding; Hoang Anh Dau; Zachary Zimmerman; Diego Furtado Silva; Abdullah Mueen; Eamonn J. Keogh (Data Min. Knowl. Discov., 2018)

Exploiting a novel algorithm and GPUs to break the ten quadrillion pairwise comparisons barrier for time series motifs and joins. - Yan Zhu 014; Zachary Zimmerman; Nader Shakibay Senobari; Chin-Chia Michael Yeh; Gareth J. Funning; Abdullah Mueen; Philip Brisk; Eamonn J. Keogh (Knowl. Inf. Syst., 2018)

Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior. - Alireza Abdoli; Amy C. Murillo; Alec C. Gerry; Eamonn J. Keogh (IEEE BigData, 2019)

Matrix Profile XIV: Scaling Time Series Motif Discovery with GPUs to Break a Quintillion Pairwise Comparisons a Day and Beyond. - Zachary Zimmerman; Kaveh Kamgar; Nader Shakibay Senobari; Brian Crites; Gareth J. Funning; Philip Brisk; Eamonn J. Keogh (SoCC, 2019)

Matrix Profile XVI: Efficient and Effective Labeling of Massive Time Series Archives. - Frank Madrid; Shailendra Singh 004; Quentin Chesnais; Kerry Mauck; Eamonn J. Keogh (DSAA, 2019)

Matrix Profile XX: Finding and Visualizing Time Series Motifs of All Lengths using the Matrix Profile. - Frank Madrid; Shima Imani; Ryan Mercer; Zachary Zimmerman; Nader Shakibay Senobari; Eamonn J. Keogh (ICBK, 2019)

Matrix Profile XIX: Time Series Semantic Motifs: A New Primitive for Finding Higher-Level Structure in Time Series. - Shima Imani; Eamonn J. Keogh (ICDM, 2019)

Matrix Profile XV: Exploiting Time Series Consensus Motifs to Find Structure in Time Series Sets. - Kaveh Kamgar; Shaghayegh Gharghabi; Eamonn J. Keogh (ICDM, 2019)

Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile. - Zachary Zimmerman; Nader Shakibay Senobari; Gareth J. Funning; Evangelos E. Papalexakis; Samet Oymak; Philip Brisk; Eamonn J. Keogh (ICDM, 2019)

Online Amnestic DTW to allow Real-Time Golden Batch Monitoring. - Chin-Chia Michael Yeh; Yan Zhu 014; Hoang Anh Dau; Amirali Darvishzadeh; Mikhail Noskov; Eamonn J. Keogh (KDD, 2019)

Putting the Human in the Time Series Analytics Loop. - Shima Imani; Sara Alaee; Eamonn J. Keogh (WWW (Companion Volume), 2019)

Time series classification for varying length series. - Chang Wei Tan; François Petitjean; Eamonn J. Keogh; Geoffrey I. Webb (CoRR, 2019)

Domain agnostic online semantic segmentation for multi-dimensional time series. - Shaghayegh Gharghabi; Chin-Chia Michael Yeh; Yifei Ding; Wei Ding 003; Paul Hibbing; Samuel LaMunion; Andrew Kaplan; Scott E. Crouter; Eamonn J. Keogh (Data Min. Knowl. Discov., 2019)

Correction to: Domain agnostic online semantic segmentation for multi-dimensional time series. - Shaghayegh Gharghabi; Chin-Chia Michael Yeh; Yifei Ding; Wei Ding 003; Paul Hibbing; Samuel LaMunion; Andrew Kaplan; Scott E. Crouter; Eamonn J. Keogh (Data Min. Knowl. Discov., 2019)

The UCR time series archive. - Hoang Anh Dau; Anthony J. Bagnall; Kaveh Kamgar; Chin-Chia Michael Yeh; Yan Zhu 014; Shaghayegh Gharghabi; Chotirat Ann Ratanamahatana; Eamonn J. Keogh (IEEE CAA J. Autom. Sinica, 2019)

Introducing time series chains: a new primitive for time series data mining. - Yan Zhu 014; Makoto Imamura; Daniel Nikovski; Eamonn J. Keogh (Knowl. Inf. Syst., 2019)

Fast Similarity Matrix Profile for Music Analysis and Exploration. - Diego Furtado Silva; Chin-Chia Michael Yeh; Yan Zhu 014; Gustavo E. A. P. A. Batista; Eamonn J. Keogh (IEEE Trans. Multim., 2019)

Matrix Profile XVII: Indexing the Matrix Profile to Allow Arbitrary Range Queries. - Yan Zhu 014; Chin-Chia Michael Yeh; Zachary Zimmerman; Eamonn J. Keogh (ICDE, 2020)

Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW. - Sara Alaee; Kaveh Kamgar; Eamonn J. Keogh (ICDM, 2020)

MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives. - Takaaki Nakamura; Makoto Imamura; Ryan Mercer; Eamonn J. Keogh (ICDM, 2020)

Fitbit for Chickens?: Time Series Data Mining Can Increase the Productivity of Poultry Farms. - Alireza Abdoli; Sara Alaee; Shima Imani; Amy C. Murillo; Alec C. Gerry; Leslie Hickle; Eamonn J. Keogh (KDD, 2020)

Matrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness. - Makoto Imamura; Takaaki Nakamura; Eamonn J. Keogh (KDD, 2020)

Features or Shape? Tackling the False Dichotomy of Time Series Classification. - Sara Alaee; Alireza Abdoli; Christian R. Shelton; Amy C. Murillo; Alec C. Gerry; Eamonn J. Keogh (SDM, 2020)

Natura: Towards Conversational Analytics for Comparing and Contrasting Time Series. - Shima Imani; Eamonn J. Keogh (WWW (Companion Volume), 2020)

An ultra-fast time series distance measure to allow data mining in more complex real-world deployments. - Shaghayegh Gharghabi; Shima Imani; Anthony J. Bagnall; Amirali Darvishzadeh; Eamonn J. Keogh (Data Min. Knowl. Discov., 2020)

Introducing time series snippets: a new primitive for summarizing long time series. - Shima Imani; Frank Madrid; Wei Ding 003; Scott E. Crouter; Eamonn J. Keogh (Data Min. Knowl. Discov., 2020)

Matrix profile goes MAD: variable-length motif and discord discovery in data series. - Michele Linardi; Yan Zhu 014; Themis Palpanas; Eamonn J. Keogh (Data Min. Knowl. Discov., 2020)

The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code. - Yan Zhu 014; Shaghayegh Gharghabi; Diego Furtado Silva; Hoang Anh Dau; Chin-Chia Michael Yeh; Nader Shakibay Senobari; Abdulaziz Almaslukh; Kaveh Kamgar; Zachary Zimmerman; Gareth J. Funning; Abdullah Mueen; Eamonn J. Keogh (Data Min. Knowl. Discov., 2020)

Matrix Profile Index Approximation for Streaming Time Series. - Maryam Shahcheraghi; Trevor Cappon; Samet Oymak; Evangelos E. Papalexakis; Eamonn J. Keogh; Zachary Zimmerman; Philip Brisk (IEEE BigData, 2021)

Shape-Based Telemetry Approach for Distracted Driving Behavior Detection. - Ryan Mercer; Seyhan Ucar; Eamonn J. Keogh (CSCN, 2021)

FastDTW is approximate and Generally Slower than the Algorithm it Approximates (Extended Abstract). - Renjie Wu 001; Eamonn J. Keogh (ICDE, 2021)

Matrix Profile XXIII: Contrast Profile: A Novel Time Series Primitive that Allows Real World Classification. - Ryan Mercer; Sara Alaee; Alireza Abdoli; Shailendra Singh 004; Amy C. Murillo; Eamonn J. Keogh (ICDM, 2021)

Time series motifs discovery under DTW allows more robust discovery of conserved structure. - Sara Alaee; Ryan Mercer; Kaveh Kamgar; Eamonn J. Keogh (Data Min. Knowl. Discov., 2021)

Matrix Profile IX: Admissible Time Series Motif Discovery With Missing Data. - Yan Zhu 014; Abdullah Mueen; Eamonn J. Keogh (IEEE Trans. Knowl. Data Eng., 2021)

Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series. - Audrey Der; Chin-Chia Michael Yeh; Renjie Wu 001; Junpeng Wang 001; Yan Zheng 001; Zhongfang Zhuang; Liang Wang 047; Wei Zhang 189; Eamonn J. Keogh (ICKG, 2022)

When is Early Classification of Time Series Meaningful? (Extended Abstract). - Renjie Wu 001; Audrey Der; Eamonn J. Keogh (ICDE, 2022)

Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress (Extended Abstract). - Renjie Wu 001; Eamonn J. Keogh (ICDE, 2022)

Matrix Profile XXV: Introducing Novelets: A Primitive that Allows Online Detection of Emerging Behaviors in Time Series. - Ryan Mercer; Eamonn J. Keogh (ICDM, 2022)

Matrix Profile XXVI: Mplots: Scaling Time Series Similarity Matrices to Massive Data. - Maryam Shahcheraghi; Ryan Mercer; João Manuel De Almeida Rodrigues; Audrey Der; Hugo Filipe Silveira Gamboa; Zachary Zimmerman; Eamonn J. Keogh (ICDM, 2022)

Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams. - Yue Lu 003; Renjie Wu 001; Abdullah Mueen; Maria A. Zuluaga; Eamonn J. Keogh (KDD, 2022)

Error-bounded Approximate Time Series Joins using Compact Dictionary Representations of Time Series. - Chin-Chia Michael Yeh; Yan Zheng 001; Junpeng Wang 001; Huiyuan Chen; Zhongfang Zhuang; Wei Zhang 189; Eamonn J. Keogh (SDM, 2022)

Introducing the contrast profile: a novel time series primitive that allows real world classification. - Ryan Mercer; Sara Alaee; Alireza Abdoli; Nader Shakibay Senobari; Shailendra Singh 004; Amy C. Murillo; Eamonn J. Keogh (Data Min. Knowl. Discov., 2022)

FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates. - Renjie Wu 001; Eamonn J. Keogh (IEEE Trans. Knowl. Data Eng., 2022)

Time Series Synthesis Using the Matrix Profile for Anonymization. - Audrey Der; Chin-Chia Michael Yeh; Yan Zheng 001; Junpeng Wang 001; Huiyuan Chen; Zhongfang Zhuang; Liang Wang 047; Wei Zhang 189; Eamonn J. Keogh (IEEE Big Data, 2023)

Ego-Network Transformer for Subsequence Classification in Time Series Data. - Chin-Chia Michael Yeh; Huiyuan Chen; Yujie Fan; Xin Dai; Yan Zheng 001; Vivian Lai; Junpeng Wang 001; Zhongfang Zhuang; Liang Wang 047; Wei Zhang 189; Eamonn J. Keogh (IEEE Big Data, 2023)

Sketching Multidimensional Time Series for Fast Discord Mining. - Chin-Chia Michael Yeh; Yan Zheng 001; Menghai Pan; Huiyuan Chen; Zhongfang Zhuang; Junpeng Wang 001; Liang Wang 047; Wei Zhang 189; Jeff M. Phillips; Eamonn J. Keogh (IEEE Big Data, 2023)

Feature Extraction Accelerator for Streaming Time Series. - Prithviraj Yuvaraj; Amin Akalantar; Eamonn J. Keogh; Philip Brisk (FCCM, 2023)

Matrix Profile XXX: MADRID: A Hyper-Anytime and Parameter-Free Algorithm to Find Time Series Anomalies of all Lengths. - Yue Lu 003; Thirumalai Vinjamoor Akhil Srinivas; Takaaki Nakamura; Makoto Imamura; Eamonn J. Keogh (ICDM, 2023)

Matrix Profile XXIX: C22MP, Fusing catch 22 and the Matrix Profile to Produce an Efficient and Interpretable Anomaly Detector. - Sadaf Tafazoli; Yue Lu 003; Renjie Wu 001; Thirumalai Vinjamoor Akhil Srinivas; Hannah Dela Cruz; Ryan Mercer; Eamonn J. Keogh (ICDM, 2023)

Tailgating Behavior Detection On Rear Vehicles. - Seyhan Ucar; Ryan Mercer; Eamonn J. Keogh (ITSC, 2023)

Getting an h-Index of 100 in 20 Years or Less! - Eamonn J. Keogh (KDD, 2023)

Matrix Profile XXVIII: Discovering Multi-Dimensional Time Series Anomalies with K of N Anomaly Detection†. - Sadaf Tafazoli; Eamonn J. Keogh (SDM, 2023)

Time Series Data Mining: A Unifying View. - Eamonn J. Keogh (Proc. VLDB Endow., 2023)

DAMP: accurate time series anomaly detection on trillions of datapoints and ultra-fast arriving data streams. - Yue Lu 003; Renjie Wu 001; Abdullah Mueen; Maria A. Zuluaga; Eamonn J. Keogh (Data Min. Knowl. Discov., 2023)

MERLIN++: parameter-free discovery of time series anomalies. - Takaaki Nakamura; Ryan Mercer; Makoto Imamura; Eamonn J. Keogh (Data Min. Knowl. Discov., 2023)

When is Early Classification of Time Series Meaningful? - Renjie Wu 001; Audrey Der; Eamonn J. Keogh (IEEE Trans. Knowl. Data Eng., 2023)

Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress. - Renjie Wu 001; Eamonn J. Keogh (IEEE Trans. Knowl. Data Eng., 2023)

PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies. - Audrey Der; Chin-Chia Michael Yeh; Yan Zheng 001; Junpeng Wang 001; Zhongfang Zhuang; Liang Wang 047; Wei Zhang 189; Eamonn J. Keogh (SDM, 2024)

A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining. - Audrey Der; Chin-Chia Michael Yeh; Xin Dai; Huiyuan Chen; Yan Zheng 001; Yujie Fan; Zhongfang Zhuang; Vivian Lai; Junpeng Wang 001; Liang Wang 047; Wei Zhang 189; Eamonn J. Keogh (CoRR, 2024)

Matrix Profile for Anomaly Detection on Multidimensional Time Series. - Chin-Chia Michael Yeh; Audrey Der; Uday Singh Saini; Vivian Lai; Yan Zheng 001; Junpeng Wang 001; Xin Dai; Zhongfang Zhuang; Yujie Fan; Huiyuan Chen; Prince Osei Aboagye; Liang Wang 047; et al. (CoRR, 2024)

Novelets: a new primitive that allows online detection of emerging behaviors in time series. - Ryan Mercer; Eamonn J. Keogh (Knowl. Inf. Syst., 2024)

C22MP: the marriage of catch22 and the matrix profile creates a fast, efficient and interpretable anomaly detector. - Sadaf Tafazoli; Yue Lu 003; Renjie Wu 001; Thirumalai Vinjamoor Akhil Srinivas; Hannah Dela Cruz; Ryan Mercer; Eamonn J. Keogh (Knowl. Inf. Syst., 2024)

Introducing Mplots: scaling time series recurrence plots to massive datasets. - Maryam Shahcheraghi; Ryan Mercer; João Manuel De Almeida Rodrigues; Audrey Der; Hugo Filipe Silveira Gamboa; Zachary Zimmerman; Kerry Mauck; Eamonn J. Keogh (J. Big Data, 2024)