DAMP: accurate time series anomaly detection on trillions of datapoints and ultra-fast arriving data streams
Time series anomaly detection is one of the most active areas of research in data mining, with dozens of new approaches been suggested each year. In spite of all these creative solutions proposed for this problem, recent empirical evidence suggests that the time series discord , a relatively simple...
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| Published in | Data mining and knowledge discovery Vol. 37; no. 2; pp. 627 - 669 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2023
Springer Nature B.V Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1384-5810 1573-756X |
| DOI | 10.1007/s10618-022-00911-7 |
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| Abstract | Time series anomaly detection is one of the most active areas of research in data mining, with dozens of new approaches been suggested each year. In spite of all these creative solutions proposed for this problem, recent empirical evidence suggests that the
time series discord
, a relatively simple twenty-year old distance-based technique, remains among the state-of-art techniques. While there are many algorithms for computing the time series discords, they all have limitations. First, they are limited to the batch case, whereas the online case is more actionable. Second, these algorithms exhibit poor scalability beyond tens of thousands of datapoints. In this work we introduce DAMP, a novel algorithm that addresses both these issues. DAMP computes exact left-discords on fast arriving streams, at up to 300,000 Hz using a commodity desktop. This allows us to find time series discords in datasets with trillions of datapoints for the first time. We will demonstrate the utility of our algorithm with the most ambitious set of time series anomaly detection experiments ever conducted. We will further show that our speedup improvements can be applied in the multidimensional case. |
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| AbstractList | Time series anomaly detection is one of the most active areas of research in data mining, with dozens of new approaches been suggested each year. In spite of all these creative solutions proposed for this problem, recent empirical evidence suggests that the time series discord, a relatively simple twenty-year old distance-based technique, remains among the state-of-art techniques. While there are many algorithms for computing the time series discords, they all have limitations. First, they are limited to the batch case, whereas the online case is more actionable. Second, these algorithms exhibit poor scalability beyond tens of thousands of datapoints. In this work we introduce DAMP, a novel algorithm that addresses both these issues. DAMP computes exact left-discords on fast arriving streams, at up to 300,000 Hz using a commodity desktop. This allows us to find time series discords in datasets with trillions of datapoints for the first time. We will demonstrate the utility of our algorithm with the most ambitious set of time series anomaly detection experiments ever conducted. We will further show that our speedup improvements can be applied in the multidimensional case. Time series anomaly detection is one of the most active areas of research in data mining, with dozens of new approaches been suggested each year. In spite of all these creative solutions proposed for this problem, recent empirical evidence suggests that the time series discord , a relatively simple twenty-year old distance-based technique, remains among the state-of-art techniques. While there are many algorithms for computing the time series discords, they all have limitations. First, they are limited to the batch case, whereas the online case is more actionable. Second, these algorithms exhibit poor scalability beyond tens of thousands of datapoints. In this work we introduce DAMP, a novel algorithm that addresses both these issues. DAMP computes exact left-discords on fast arriving streams, at up to 300,000 Hz using a commodity desktop. This allows us to find time series discords in datasets with trillions of datapoints for the first time. We will demonstrate the utility of our algorithm with the most ambitious set of time series anomaly detection experiments ever conducted. We will further show that our speedup improvements can be applied in the multidimensional case. |
| Author | Lu, Yue Wu, Renjie Zuluaga, Maria A. Mueen, Abdullah Keogh, Eamonn |
| Author_xml | – sequence: 1 givenname: Yue orcidid: 0000-0003-4812-9658 surname: Lu fullname: Lu, Yue email: ylu175@ucr.edu organization: University of California, Riverside – sequence: 2 givenname: Renjie surname: Wu fullname: Wu, Renjie organization: University of California, Riverside – sequence: 3 givenname: Abdullah surname: Mueen fullname: Mueen, Abdullah organization: Department of Computer Science, University of New Mexico – sequence: 4 givenname: Maria A. surname: Zuluaga fullname: Zuluaga, Maria A. organization: Data Science Department, EURECOM – sequence: 5 givenname: Eamonn surname: Keogh fullname: Keogh, Eamonn organization: University of California, Riverside |
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| CitedBy_id | crossref_primary_10_1016_j_mlwa_2024_100530 crossref_primary_10_1007_s10994_024_06712_x crossref_primary_10_1038_s44260_025_00030_6 crossref_primary_10_1016_j_is_2025_102524 |
| Cites_doi | 10.52964/AMJA.0553 10.1109/ICDM50108.2020.00147 10.1109/JAS.2019.1911747 10.1145/3219819.3219845 10.1137/1.9781611977172.21 10.1007/s10618-020-00702-y 10.1016/j.cageo.2022.105040 10.1145/3292500.3330672 10.1109/LRA.2018.2801475 10.14778/3529337.3529354 10.1109/ICDM.2018.00099 10.1109/ACCESS.2020.2990528 10.1007/s00778-021-00655-8 10.1016/j.compind.2022.103692 10.1007/978-3-030-91445-5_12 10.1109/TKDE.2021.3112126 10.1016/j.enbuild.2020.109892 10.14778/3467861.3467863 10.1145/3292500.3330650 10.1038/s41597-022-01455-7 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Distributed under a Creative Commons Attribution 4.0 International License |
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| Keywords | Streaming data Anomaly detection Time series |
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| References_xml | – reference: Higham NJ (2002) Accuracy and stability of numerical algorithms, 2 edn. ISBN: 978-0-89871-521-7 – reference: Wastewater News. Valentine’s day storm slams California, pushing water agencies to the edge. From www.news.cornell.edu/Chronicle/00/5.18.00/wireless_class.html. Retrieved Dec 1 2021 – reference: Wikipedia. Leap year problem. from https://en.wikipedia.org/wiki/Leap_year_problem. Retrieved December 1, 2021 – reference: ThillMKonenWBäckTTime series encodings with temporal convolutional networks2020ChamSpringer161173 – reference: Zheng X, Xu N, Trinh L et al (2021) PSML: a multi-scale time-series dataset for machine learning in decarbonized energy grids. arXiv preprint arXiv: 2110.06324 – reference: Doshi K, Abudalou S, Yilmaz Y (2022) TiSAT: time series anomaly transformer. arXiv:2203.05167 [cs, eess, stat] – reference: NeupaneDSeokJBearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A reviewIEEE Access20208931559317810.1109/ACCESS.2020.2990528 – reference: Mueen A, Zhu Y, Yeh M et al (2017) The fastest similarity search algorithm for time series subsequences under euclidean distance. http://www.cs.unm.edu/~mueen/FastestSimilaritySearch.htmlAccessed 24 Janurary, 2022 – reference: Palpanas T (2022) Personal communication June 4th 2022 – reference: TruongHTTaBPLeQALight-weight federated learning-based anomaly detection for time-series data in industrial control systemsComput Ind202214010369210.1016/j.compind.2022.103692 – reference: Yeh C-CM, Zhu Y, Dau HA et al (2019) Online amnestic dtw to allow real-time golden batch monitoring. pp 2604–2612 – reference: ParkJYWilsonEParkerANagyZThe good, the bad, and the ugly: data-driven load profile discord identification in a large building portfolioEnergy Build202021510.1016/j.enbuild.2020.109892 – reference: Keogh E (2021) Irrational exuberance why we should not believe 95% of papers on time series anomaly detection. In: 7th SIGKDD workshop on mining and learning from time series at SIGKDD 2021. Workshop Keynote https://www.youtube.com/watch?v=Vg1p3DouX8w&t=324s – reference: Nilsson F (2022) Joint human-machine exploration of industrial time series using the matrix profile. 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| SubjectTerms | Algorithms Anomalies Artificial Intelligence Chemistry and Earth Sciences Computer Aided Engineering Computer Science Data mining Data Mining and Knowledge Discovery Data transmission Information Storage and Retrieval Physics Statistics for Engineering Time series |
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| Title | DAMP: accurate time series anomaly detection on trillions of datapoints and ultra-fast arriving data streams |
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