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ORCIDEamonn J. Keogh
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- affiliation: University of California, Riverside, USA
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2020 – today
- 2024
[j79]Maryam Shahcheraghi, Ryan Mercer, João Manuel De Almeida Rodrigues, Audrey Der, Hugo Filipe Silveira Gamboa, Zachary Zimmerman, Kerry Mauck, Eamonn J. Keogh:
Introducing Mplots: scaling time series recurrence plots to massive datasets. J. Big Data 11(1): 96 (2024)- [j78]Ryan Mercer, Eamonn J. Keogh:
Novelets: a new primitive that allows online detection of emerging behaviors in time series. Knowl. Inf. Syst. 66(1): 59-87 (2024) - [j77]Sadaf Tafazoli, Yue Lu, Renjie Wu
, Thirumalai Vinjamoor Akhil Srinivas, Hannah Dela Cruz, Ryan Mercer, Eamonn J. Keogh:
C22MP: the marriage of catch22 and the matrix profile creates a fast, efficient and interpretable anomaly detector. Knowl. Inf. Syst. 66(8): 4789-4823 (2024) - [c201]Audrey Der, Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Zhongfang Zhuang, Vivian Lai, Junpeng Wang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining. CIKM 2024: 3719-3723 - [c200]Eamonn J. Keogh:
Time Series Data Mining: A Unifying View. DSAA 2024: 1-3 - [c199]Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies. SDM 2024: 37-45 - [i25]Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies. CoRR abs/2401.09489 (2024) - [i24]Audrey Der, Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Zhongfang Zhuang, Vivian Lai, Junpeng Wang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining. CoRR abs/2408.07869 (2024) - [i23]Chin-Chia Michael Yeh, Audrey Der, Uday Singh Saini, Vivian Lai, Yan Zheng, Junpeng Wang, Xin Dai, Zhongfang Zhuang, Yujie Fan, Huiyuan Chen, Prince Osei Aboagye, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Matrix Profile for Anomaly Detection on Multidimensional Time Series. CoRR abs/2409.09298 (2024) - 2023
- [j76]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) - [j75]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) - [j74]Eamonn J. Keogh:
Time Series Data Mining: A Unifying View. Proc. VLDB Endow. 16(12): 3861-3863 (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) - [c198]Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M. Phillips, Eamonn J. Keogh:
Sketching Multidimensional Time Series for Fast Discord Mining. IEEE Big Data 2023: 443-452 - [c197]Chin-Chia Michael Yeh, Huiyuan Chen, Yujie Fan, Xin Dai, Yan Zheng, Vivian Lai, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Ego-Network Transformer for Subsequence Classification in Time Series Data. IEEE Big Data 2023: 1242-1247 - [c196]Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Time Series Synthesis Using the Matrix Profile for Anonymization. IEEE Big Data 2023: 1908-1911 - [c195]Prithviraj Yuvaraj, Amin Akalantar, Eamonn J. Keogh, Philip Brisk:
Feature Extraction Accelerator for Streaming Time Series. FCCM 2023: 207 - [c194]Sadaf Tafazoli, Yue Lu, Renjie Wu, Thirumalai Vinjamoor Akhil Srinivas, Hannah Dela Cruz, Ryan Mercer, Eamonn J. Keogh:
Matrix Profile XXIX: C22MP, Fusing catch 22 and the Matrix Profile to Produce an Efficient and Interpretable Anomaly Detector. ICDM 2023: 568-577 - [c193]Yue Lu, Thirumalai Vinjamoor Akhil Srinivas, Takaaki Nakamura, Makoto Imamura, Eamonn J. Keogh:
Matrix Profile XXX: MADRID: A Hyper-Anytime and Parameter-Free Algorithm to Find Time Series Anomalies of all Lengths. ICDM 2023: 1199-1204 - [c192]Seyhan Ucar, Ryan Mercer, Eamonn J. Keogh:
Tailgating Behavior Detection On Rear Vehicles. ITSC 2023: 440-445 - [c191]Eamonn J. Keogh:
Getting an h-Index of 100 in 20 Years or Less! KDD 2023: 5807-5808 - [c190]Sadaf Tafazoli, Eamonn J. Keogh:
Matrix Profile XXVIII: Discovering Multi-Dimensional Time Series Anomalies with K of N Anomaly Detection†. SDM 2023: 685-693 - [i22]Chin-Chia Michael Yeh, Huiyuan Chen, Yujie Fan, Xin Dai, Yan Zheng, Vivian Lai, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Ego-Network Transformer for Subsequence Classification in Time Series Data. CoRR abs/2311.02561 (2023) - [i21]Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Time Series Synthesis Using the Matrix Profile for Anonymization. CoRR abs/2311.02563 (2023) - [i20]Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M. Phillips, Eamonn J. Keogh:
Sketching Multidimensional Time Series for Fast Discord Mining. CoRR abs/2311.03393 (2023) - 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]