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ORCIDEamonn J. Keogh
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- affiliation: University of California, Riverside, USA
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2020 – today
- 2023
[j75]Yue Lu
, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn J. Keogh:
DAMP: accurate time series anomaly detection on trillions of datapoints and ultra-fast arriving data streams. Data Min. Knowl. Discov. 37(2): 627-669 (2023)- [j74]Takaaki Nakamura, Ryan Mercer, Makoto Imamura, Eamonn J. Keogh:
MERLIN++: parameter-free discovery of time series anomalies. Data Min. Knowl. Discov. 37(2): 670-709 (2023) - [j73]Renjie Wu, Eamonn J. Keogh:
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress. IEEE Trans. Knowl. Data Eng. 35(3): 2421-2429 (2023) - [j72]Renjie Wu, Audrey Der, Eamonn J. Keogh:
When is Early Classification of Time Series Meaningful? IEEE Trans. Knowl. Data Eng. 35(3): 3253-3260 (2023) - [c191]Prithviraj Yuvaraj, Amin Akalantar, Eamonn J. Keogh, Philip Brisk:
Feature Extraction Accelerator for Streaming Time Series. FCCM 2023: 207 - [c190]Eamonn J. Keogh:
Getting an h-Index of 100 in 20 Years or Less! KDD 2023: 5807-5808 - 2022
- [j71]Ryan Mercer, Sara Alaee, Alireza Abdoli, Nader Shakibay Senobari, Shailendra Singh, Amy C. Murillo, Eamonn J. Keogh:
Introducing the contrast profile: a novel time series primitive that allows real world classification. Data Min. Knowl. Discov. 36(2): 877-915 (2022) - [j70]Renjie Wu, Eamonn J. Keogh:
FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates. IEEE Trans. Knowl. Data Eng. 34(8): 3779-3785 (2022) - [c189]Audrey Der, Chin-Chia Michael Yeh, Renjie Wu, Junpeng Wang, Yan Zheng, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series. ICKG 2022: 40-47 - [c188]Renjie Wu, Audrey Der, Eamonn J. Keogh:
When is Early Classification of Time Series Meaningful? (Extended Abstract). ICDE 2022: 1477-1478 - [c187]Renjie Wu, Eamonn J. Keogh:
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress (Extended Abstract). ICDE 2022: 1479-1480 - [c186]Ryan Mercer, Eamonn J. Keogh:
Matrix Profile XXV: Introducing Novelets: A Primitive that Allows Online Detection of Emerging Behaviors in Time Series. ICDM 2022: 338-347 - [c185]Maryam Shahcheraghi, Ryan Mercer, João Manuel De Almeida Rodrigues, Audrey Der, Hugo Filipe Silveira Gamboa, Zachary Zimmerman, Eamonn J. Keogh:
Matrix Profile XXVI: Mplots: Scaling Time Series Similarity Matrices to Massive Data. ICDM 2022: 1179-1184 - [c184]Yue Lu, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn J. Keogh:
Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams. KDD 2022: 1173-1182 - [c183]Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, Eamonn J. Keogh:
Error-bounded Approximate Time Series Joins using Compact Dictionary Representations of Time Series. SDM 2022: 181-189 - [i19]Audrey Der, Chin-Chia Michael Yeh, Renjie Wu, Junpeng Wang, Yan Zheng, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series. CoRR abs/2212.06146 (2022) - 2021
- [j69]Sara Alaee, Ryan Mercer, Kaveh Kamgar, Eamonn J. Keogh:
Time series motifs discovery under DTW allows more robust discovery of conserved structure. Data Min. Knowl. Discov. 35(3): 863-910 (2021) - [j68]Yan Zhu, Abdullah Mueen, Eamonn J. Keogh:
Matrix Profile IX: Admissible Time Series Motif Discovery With Missing Data. IEEE Trans. Knowl. Data Eng. 33(6): 2616-2626 (2021) - [c182]Maryam Shahcheraghi, Trevor Cappon, Samet Oymak, Evangelos E. Papalexakis, Eamonn J. Keogh, Zachary Zimmerman, Philip Brisk:
Matrix Profile Index Approximation for Streaming Time Series. IEEE BigData 2021: 2775-2784 - [c181]Ryan Mercer, Seyhan Ucar, Eamonn J. Keogh:
Shape-Based Telemetry Approach for Distracted Driving Behavior Detection. CSCN 2021: 118-123 - [c180]Renjie Wu, Eamonn J. Keogh:
FastDTW is approximate and Generally Slower than the Algorithm it Approximates (Extended Abstract). ICDE 2021: 2327-2328 - [c179]Ryan Mercer, Sara Alaee, Alireza Abdoli, Shailendra Singh, Amy C. Murillo, Eamonn J. Keogh:
Matrix Profile XXIII: Contrast Profile: A Novel Time Series Primitive that Allows Real World Classification. ICDM 2021: 1240-1245 - [i18]Renjie Wu, Audrey Der, Eamonn J. Keogh:
When is Early Classification of Time Series Meaningful? CoRR abs/2102.11487 (2021) - [i17]Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, Eamonn J. Keogh:
Error-bounded Approximate Time Series Joins using Compact Dictionary Representations of Time Series. CoRR abs/2112.12965 (2021) - 2020
- [j67]Yan Zhu, 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:
The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code. Data Min. Knowl. Discov. 34(4): 949-979 (2020) - [j66]Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh:
Matrix profile goes MAD: variable-length motif and discord discovery in data series. Data Min. Knowl. Discov. 34(4): 1022-1071 (2020) - [j65]Shaghayegh Gharghabi, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh:
An ultra-fast time series distance measure to allow data mining in more complex real-world deployments. Data Min. Knowl. Discov. 34(4): 1104-1135 (2020) - [j64]Shima Imani, Frank Madrid, Wei Ding, Scott E. Crouter, Eamonn J. Keogh:
Introducing time series snippets: a new primitive for summarizing long time series. Data Min. Knowl. Discov. 34(6): 1713-1743 (2020) - [c178]Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Eamonn J. Keogh:
Matrix Profile XVII: Indexing the Matrix Profile to Allow Arbitrary Range Queries. ICDE 2020: 1846-1849 - [c177]Sara Alaee, Kaveh Kamgar, Eamonn J. Keogh:
Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW. ICDM 2020: 900-905 - [c176]Takaaki Nakamura, Makoto Imamura, Ryan Mercer, Eamonn J. Keogh:
MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives. ICDM 2020: 1190-1195 - [c175]Makoto Imamura, Takaaki Nakamura, Eamonn J. Keogh:
Matrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness. KDD 2020: 1114-1122 - [c174]Alireza Abdoli, Sara Alaee, Shima Imani, Amy C. Murillo, Alec C. Gerry, Leslie Hickle, Eamonn J. Keogh:
Fitbit for Chickens?: Time Series Data Mining Can Increase the Productivity of Poultry Farms. KDD 2020: 3328-3336 - [c173]Sara Alaee, Alireza Abdoli, Christian R. Shelton, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh:
Features or Shape? Tackling the False Dichotomy of Time Series Classification. SDM 2020: 442-450 - [c172]Shima Imani, Eamonn J. Keogh:
Natura: Towards Conversational Analytics for Comparing and Contrasting Time Series. WWW (Companion Volume) 2020: 46-47 - [i16]Renjie Wu, Eamonn J. Keogh:
FastDTW is approximate and Generally Slower than the Algorithm it Approximates. CoRR abs/2003.11246 (2020) - [i15]Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh:
VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series. CoRR abs/2008.13432 (2020) - [i14]Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh:
Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series. CoRR abs/2008.13447 (2020) - [i13]Sara Alaee, Kaveh Kamgar, Eamonn J. Keogh:
Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW. CoRR abs/2009.07907 (2020) - [i12]Renjie Wu, Eamonn J. Keogh:
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress. CoRR abs/2009.13807 (2020)
2010 – 2019
- 2019
- [j63]Shaghayegh Gharghabi, Chin-Chia Michael Yeh, Yifei Ding, Wei Ding, Paul Hibbing, Samuel LaMunion, Andrew Kaplan, Scott E. Crouter, Eamonn J. Keogh:
Domain agnostic online semantic segmentation for multi-dimensional time series. Data Min. Knowl. Discov. 33(1): 96-130 (2019) - [j62]Shaghayegh Gharghabi, Chin-Chia Michael Yeh, Yifei Ding, Wei Ding, Paul Hibbing, Samuel LaMunion, Andrew Kaplan, Scott E. Crouter, Eamonn J. Keogh:
Correction to: Domain agnostic online semantic segmentation for multi-dimensional time series. Data Min. Knowl. Discov. 33(6): 1981-1982 (2019) - [j61]Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn J. Keogh:
The UCR time series archive. IEEE CAA J. Autom. Sinica 6(6): 1293-1305 (2019) - [j60]Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn J. Keogh:
Introducing time series chains: a new primitive for time series data mining. Knowl. Inf. Syst. 60(2): 1135-1161 (2019) - [j59]Diego Furtado Silva, Chin-Chia Michael Yeh, Yan Zhu, Gustavo E. A. P. A. Batista, Eamonn J. Keogh:
Fast Similarity Matrix Profile for Music Analysis and Exploration. IEEE Trans. Multim. 21(1): 29-38 (2019) - [c171]Alireza Abdoli, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh:
Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior. IEEE BigData 2019: 5962-5964 - [c170]Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth J. Funning, Philip Brisk, Eamonn J. Keogh:
Matrix Profile XIV: Scaling Time Series Motif Discovery with GPUs to Break a Quintillion Pairwise Comparisons a Day and Beyond. SoCC 2019: 74-86 - [c169]Frank Madrid, Shailendra Singh, Quentin Chesnais, Kerry Mauck, Eamonn J. Keogh:
Matrix Profile XVI: Efficient and Effective Labeling of Massive Time Series Archives. DSAA 2019: 463-472 - [c168]Frank Madrid, Shima Imani, Ryan Mercer, Zachary Zimmerman, Nader Shakibay Senobari, Eamonn J. Keogh:
Matrix Profile XX: Finding and Visualizing Time Series Motifs of All Lengths using the Matrix Profile. ICBK 2019: 175-182 - [c167]Shima Imani, Eamonn J. Keogh:
Matrix Profile XIX: Time Series Semantic Motifs: A New Primitive for Finding Higher-Level Structure in Time Series. ICDM 2019: 329-338 - [c166]Zachary Zimmerman, Nader Shakibay Senobari, Gareth J. Funning, Evangelos E. Papalexakis, Samet Oymak, Philip Brisk, Eamonn J. Keogh:
Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile. ICDM 2019: 936-945 - [c165]Kaveh Kamgar, Shaghayegh Gharghabi, Eamonn J. Keogh:
Matrix Profile XV: Exploiting Time Series Consensus Motifs to Find Structure in Time Series Sets. ICDM 2019: 1156-1161 - [c164]Chin-Chia Michael Yeh, Yan Zhu, Hoang Anh Dau, Amirali Darvishzadeh, Mikhail Noskov, Eamonn J. Keogh:
Online Amnestic DTW to allow Real-Time Golden Batch Monitoring. KDD 2019: 2604-2612 - [c163]Shima Imani, Sara Alaee, Eamonn J. Keogh:
Putting the Human in the Time Series Analytics Loop. WWW (Companion Volume) 2019: 635-644 - [i11]Chang Wei Tan, François Petitjean, Eamonn J. Keogh, Geoffrey I. Webb:
Time series classification for varying length series. CoRR abs/1910.04341 (2019) - [i10]Alireza Abdoli, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh:
Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior. CoRR abs/1912.05913 (2019) - [i9]Sara Alaee, Alireza Abdoli, Christian R. Shelton, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh:
Features or Shape? Tackling the False Dichotomy of Time Series Classification. CoRR abs/1912.09614 (2019) - 2018
- [j58]Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Zachary Zimmerman, Diego Furtado Silva, Abdullah Mueen, Eamonn J. Keogh:
Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile. Data Min. Knowl. Discov. 32(1): 83-123 (2018) - [j57]Diego Furtado Silva, Rafael Giusti, Eamonn J. Keogh, Gustavo E. A. P. A. Batista:
Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Min. Knowl. Discov. 32(4): 988-1016 (2018) - [j56]Hoang Anh Dau, Diego Furtado Silva, François Petitjean, Germain Forestier, Anthony J. Bagnall, Abdullah Mueen, Eamonn J. Keogh:
Optimizing dynamic time warping's window width for time series data mining applications. Data Min. Knowl. Discov. 32(4): 1074-1120 (2018) - [j55]Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth J. Funning, Abdullah Mueen, Philip Brisk, Eamonn J. Keogh:
Exploiting a novel algorithm and GPUs to break the ten quadrillion pairwise comparisons barrier for time series motifs and joins. Knowl. Inf. Syst. 54(1): 203-236 (2018) - [c162]Shima Imani, Frank Madrid, Wei Ding, Scott E. Crouter, Eamonn J. Keogh:
Matrix Profile XIII: Time Series Snippets: A New Primitive for Time Series Data Mining. ICBK 2018: 382-389 - [c161]Rodica Neamtu, Ramoza Ahsan, Elke A. Rundensteiner, Gábor N. Sárközy, Eamonn J. Keogh, Hoang Anh Dau, Cuong Nguyen, Charles Lovering:
Generalized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances. ICDE 2018: 521-532 - [c160]Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Kaveh Kamgar, Eamonn J. Keogh:
Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds. ICDM 2018: 837-846 - [c159]Shaghayegh Gharghabi, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh:
Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios. ICDM 2018: 965-970 - [c158]Alireza Abdoli, Amy C. Murillo, Chin-Chia Michael Yeh, Alec C. Gerry, Eamonn J. Keogh:
Time Series Classification to Improve Poultry Welfare. ICMLA 2018: 635-642 - [c157]Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn J. Keogh:
Time Series Chains: A Novel Tool for Time Series Data Mining. IJCAI 2018: 5414-5418 - [c156]Yilin Shen, Yanping Chen, Eamonn J. Keogh, Hongxia Jin:
Accelerating Time Series Searching with Large Uniform Scaling. SDM 2018: 234-242 - [c155]Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh:
Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series. SIGMOD Conference 2018: 1053-1066 - [c154]Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh:
VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series. SIGMOD Conference 2018: 1757-1760 - [i8]Yan Zhu, Abdullah Mueen, Eamonn J. Keogh:
Admissible Time Series Motif Discovery with Missing Data. CoRR abs/1802.05472 (2018) - [i7]Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn J. Keogh:
The UCR Time Series Archive. CoRR abs/1810.07758 (2018) - [i6]Anthony J. Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, Eamonn J. Keogh:
The UEA multivariate time series classification archive, 2018. CoRR abs/1811.00075 (2018) - [i5]Chin-Chia Michael Yeh, Yan Zhu, Evangelos E. Papalexakis, Abdullah Mueen, Eamonn J. Keogh:
Representation Learning by Reconstructing Neighborhoods. CoRR abs/1811.01557 (2018) - [i4]Alireza Abdoli, Amy C. Murillo, Chin-Chia Michael Yeh, Alec C. Gerry, Eamonn J. Keogh:
Time Series Classification to Improve Poultry Welfare. CoRR abs/1811.03149 (2018) - 2017
- [j54]Mohammad Shokoohi-Yekta, Bing Hu, Hongxia Jin, Jun Wang, Eamonn J. Keogh:
Generalizing DTW to the multi-dimensional case requires an adaptive approach. Data Min. Knowl. Discov. 31(1): 1-31 (2017) - [j53]Usue Mori, Alexander Mendiburu, Eamonn J. Keogh, José Antonio Lozano:
Reliable early classification of time series based on discriminating the classes over time. Data Min. Knowl. Discov. 31(1): 233-263 (2017) - [j52]Anthony J. Bagnall, Jason Lines, Aaron Bostrom, James Large, Eamonn J. Keogh:
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 31(3): 606-660 (2017) - [j51]Chin-Chia Michael Yeh, Nickolas Kavantzas, Eamonn J. Keogh:
Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes. Proc. VLDB Endow. 10(12): 1802-1812 (2017) - [c153]Hoang Anh Dau, Diego Furtado Silva, François Petitjean, Germain Forestier, Anthony J. Bagnall, Eamonn J. Keogh:
Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series. IEEE BigData 2017: 917-922 - [c152]Yilin Shen, Yanping Chen, Eamonn J. Keogh, Hongxia Jin:
Searching Time Series with Invariance to Large Amounts of Uniform Scaling. ICDE 2017: 111-114 - [c151]Shaghayegh Gharghabi, Yifei Ding, Chin-Chia Michael Yeh, Kaveh Kamgar, Liudmila Ulanova, Eamonn J. Keogh:
Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels. ICDM 2017: 117-126 - [c150]Chin-Chia Michael Yeh, Nickolas Kavantzas, Eamonn J. Keogh:
Matrix Profile VI: Meaningful Multidimensional Motif Discovery. ICDM 2017: 565-574 - [c149]Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn J. Keogh:
Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining (Best Student Paper Award). ICDM 2017: 695-704 - [c148]Germain Forestier, François Petitjean, Hoang Anh Dau, Geoffrey I. Webb, Eamonn J. Keogh:
Generating Synthetic Time Series to Augment Sparse Datasets. ICDM 2017: 865-870 - [c147]Hoang Anh Dau, Eamonn J. Keogh:
Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery. KDD 2017: 125-134 - [c146]Yifei Ding, Eamonn J. Keogh:
Query Suggestion to allow Intuitive Interactive Search in Multidimensional Time Series. SSDBM 2017: 18:1-18:11 - [r12]Eamonn J. Keogh:
Indexing and Mining Time Series Data. Encyclopedia of GIS 2017: 933-939 - [r11]Eamonn J. Keogh, Abdullah Mueen:
Curse of Dimensionality. Encyclopedia of Machine Learning and Data Mining 2017: 314-315 - [r10]Eamonn J. Keogh:
Instance-Based Learning. Encyclopedia of Machine Learning and Data Mining 2017: 672-673 - [r9]Eamonn J. Keogh:
Nearest Neighbor. Encyclopedia of Machine Learning and Data Mining 2017: 897 - [r8]Eamonn J. Keogh:
Time Series. Encyclopedia of Machine Learning and Data Mining 2017: 1274-1275 - 2016
- [j50]Jesin Zakaria, Abdullah Mueen, Eamonn J. Keogh, Neal E. Young:
Accelerating the discovery of unsupervised-shapelets. Data Min. Knowl. Discov. 30(1): 243-281 (2016) - [j49]Bing Hu, Yanping Chen, Eamonn J. Keogh:
Classification of streaming time series under more realistic assumptions. Data Min. Knowl. Discov. 30(2): 403-437 (2016) - [j48]Yan Zhu, Eamonn J. Keogh:
Irrevocable-choice algorithms for sampling from a stream. Data Min. Knowl. Discov. 30(5): 998-1023 (2016) - [j47]François Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen, Eamonn J. Keogh:
Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm. Knowl. Inf. Syst. 47(1): 1-26 (2016) - [c145]Hoang Anh Dau, Nurjahan Begum, Eamonn J. Keogh:
Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping. CIKM 2016: 999-1008 - [c144]Chin-Chia Michael Yeh, Helga Van Herle, Eamonn J. Keogh:
Matrix Profile III: The Matrix Profile Allows Visualization of Salient Subsequences in Massive Time Series. ICDM 2016: 579-588 - [c143]Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth J. Funning, Abdullah Mueen, Philip Brisk, Eamonn J. Keogh:
Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins. ICDM 2016: 739-748 - [c142]Diego Furtado Silva, Gustavo E. A. P. A. Batista, Eamonn J. Keogh:
Prefix and Suffix Invariant Dynamic Time Warping. ICDM 2016: 1209-1214 - [c141]Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, Eamonn J. Keogh:
Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets. ICDM 2016: 1317-1322 - [c140]Diego Furtado Silva, Chin-Chia Michael Yeh, Gustavo E. A. P. A. Batista, Eamonn J. Keogh:
SiMPle: Assessing Music Similarity Using Subsequences Joins. ISMIR 2016: 23-29 - [c139]Abdullah Mueen, Eamonn J. Keogh:
Extracting Optimal Performance from Dynamic Time Warping. KDD 2016: 2129-2130 - [c138]Liudmila Ulanova, Nurjahan Begum, Mohammad Shokoohi-Yekta, Eamonn J. Keogh:
Clustering in the Face of Fast Changing Streams. SDM 2016: 1-9 - [i3]Nurjahan Begum, Liudmila Ulanova, Hoang Anh Dau, Jun Wang, Eamonn J. Keogh:
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy. CoRR abs/1612.00637 (2016) - 2015
- [j46]Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn J. Keogh:
Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series. Data Min. Knowl. Discov. 29(2): 358-399 (2015) - [j45]Yanping Chen, Yuan Hao, Thanawin Rakthanmanon, Jesin Zakaria, Bing Hu, Eamonn J. Keogh:
A general framework for never-ending learning from time series streams. Data Min. Knowl. Discov. 29(6): 1622-1664 (2015) - [j44]Diego Furtado Silva, Vinícius M. A. de Souza, Daniel P. W. Ellis, Eamonn J. Keogh, Gustavo E. A. P. A. Batista:
Exploring Low Cost Laser Sensors to Identify Flying Insect Species - Evaluation of Machine Learning and Signal Processing Methods. J. Intell. Robotic Syst. 80(Supplement-1): 313-330 (2015) - [j43]Bing Hu, Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Eamonn J. Keogh:
Establishing the provenance of historical manuscripts with a novel distance measure. Pattern Anal. Appl. 18(2): 313-331 (2015) - [c137]Nurjahan Begum, Liudmila Ulanova, Jun Wang, Eamonn J. Keogh:
Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy. KDD 2015: 49-58 - [c136]Mohammad Shokoohi-Yekta, Yanping Chen, Bilson J. L. Campana, Bing Hu, Jesin Zakaria, Eamonn J. Keogh:
Discovery of Meaningful Rules in Time Series. KDD 2015: 1085-1094 - [c135]Liudmila Ulanova, Tan Yan, Haifeng Chen, Guofei Jiang, Eamonn J. Keogh, Kai Zhang:
Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems. KDD 2015: 2167-2176 - [c134]