Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress R Wu, E Keogh IEEE Transactions on Knowledge and Data Engineering 35 (3), 2421-2429, 2021 | 179 | 2021 |
FastDTW is approximate and Generally Slower than the Algorithm it Approximates R Wu, EJ Keogh IEEE Transactions on Knowledge and Data Engineering 34 (8), 3779-3785, 2020 | 42 | 2020 |
Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams Y Lu, R Wu, A Mueen, MA Zuluaga, E Keogh Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 30 | 2022 |
When is Early Classification of Time Series Meaningful? R Wu, A Der, E Keogh IEEE Transactions on Knowledge and Data Engineering 35 (3), 3253-3260, 2021 | 14 | 2021 |
DAMP: Accurate Time Series Anomaly Detection on Trillions of Datapoints and Ultra-fast Arriving Data Streams Y Lu, R Wu, A Mueen, MA Zuluaga, E Keogh Data Mining and Knowledge Discovery 37 (2), 627-669, 2023 | 6 | 2023 |
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series A Der, CCM Yeh, R Wu, J Wang, Y Zheng, Z Zhuang, L Wang, W Zhang, ... 2022 IEEE International Conference on Knowledge Graph (ICKG), 40-47, 2022 | 1 | 2022 |
Matrix Profile XXIX: C22MP, Fusing catch 22 and the Matrix Profile to Produce an Efficient and Interpretable Anomaly Detector S Tafazoli, Y Lu, R Wu, TVA Srinivas, HD Cruz, R Mercer, E Keogh 2023 IEEE International Conference on Data Mining (ICDM), 568-577, 2023 | | 2023 |