| The UCR time series archive HA Dau, A Bagnall, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... IEEE/CAA Journal of Automatica Sinica 6 (6), 1293-1305, 2019 | 1108 | 2019 |
| Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets CCM Yeh, Y Zhu, L Ulanova, N Begum, Y Ding, HA Dau, DF Silva, ... 2016 IEEE 16th international conference on data mining (ICDM), 1317-1322, 2016 | 890 | 2016 |
| The UEA multivariate time series classification archive, 2018 A Bagnall, HA Dau, J Lines, M Flynn, J Large, A Bostrom, P Southam, ... arXiv preprint arXiv:1811.00075, 2018 | 588 | 2018 |
| The UCR time series classification archive HA Dau, E Keogh, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... URL https://www. cs. ucr. edu/~ eamonn/time_series_data_2018, 2018 | 318 | 2018 |
| Generating synthetic time series to augment sparse datasets G Forestier, F Petitjean, HA Dau, GI Webb, E Keogh 2017 IEEE international conference on data mining (ICDM), 865-870, 2017 | 225 | 2017 |
| Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile CCM Yeh, Y Zhu, L Ulanova, N Begum, Y Ding, HA Dau, Z Zimmerman, ... Data Mining and Knowledge Discovery 32, 83-123, 2018 | 177 | 2018 |
| Anomaly detection using replicator neural networks trained on examples of one class HA Dau, V Ciesielski, A Song Asia-Pacific conference on simulated evolution and learning, 311-322, 2014 | 117 | 2014 |
| Optimizing dynamic time warping’s window width for time series data mining applications HA Dau, DF Silva, F Petitjean, G Forestier, A Bagnall, A Mueen, E Keogh Data mining and knowledge discovery 32, 1074-1120, 2018 | 107 | 2018 |
| Matrix profile v: A generic technique to incorporate domain knowledge into motif discovery HA Dau, E Keogh Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 98 | 2017 |
| Semi-supervision dramatically improves time series clustering under dynamic time warping HA Dau, N Begum, E Keogh Proceedings of the 25th ACM international on conference on information and …, 2016 | 55 | 2016 |
| Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series HA Dau, DF Silva, F Petitjean, G Forestier, A Bagnall, E Keogh 2017 IEEE international conference on big data (big data), 917-922, 2017 | 32 | 2017 |
| Generalized dynamic time warping: Unleashing the warping power hidden in point-wise distances R Neamtu, R Ahsan, EA Rundensteiner, G Sarkozy, E Keogh, HA Dau, ... 2018 IEEE 34th International Conference on Data Engineering (ICDE), 521-532, 2018 | 30 | 2018 |
| Online amnestic dtw to allow real-time golden batch monitoring CCM Yeh, Y Zhu, HA Dau, A Darvishzadeh, M Noskov, E Keogh Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 24 | 2019 |
| Anomaly detection for insertion tasks in robotic assembly using Gaussian process models D Romeres, DK Jha, W Yerazunis, D Nikovski, HA Dau 2019 18th European Control Conference (ECC), 1017-1022, 2019 | 20 | 2019 |
| Phone based fall detection by genetic programming HA Dau, FD Salim, A Song, L Hedin, M Hamilton Proceedings of the 13th International Conference on Mobile and Ubiquitous …, 2014 | 11 | 2014 |
| A general framework for density based time series clustering exploiting a novel admissible pruning strategy N Begum, L Ulanova, HA Dau, J Wang, E Keogh arXiv preprint arXiv:1612.00637, 2016 | 9 | 2016 |
| Evolving PCB visual inspection programs using genetic programming F Xie, AH Dau, AL Uitdenbogerd, A Song 2013 28th International Conference on Image and Vision Computing New Zealand …, 2013 | 7 | 2013 |
| Anomaly Detection in Discrete Manufacturing Systems using Event Relationship Tables. E Laftchiev, X Sun, HA Dau, D Nikovski DX, 2018 | 3 | 2018 |
| Genetic Programming for Channel Selection from Multi-stream Sensor Data with Application on Learning Risky Driving Behaviours HA Dau, A Song, F Xie, FD Salim, V Ciesielski Simulated Evolution and Learning: 10th International Conference, SEAL 2014 …, 2014 | | 2014 |
Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideEmail confirmado em cs.ucr.edu
