article

Finding the most unusual time series subsequence: algorithms and applications

Authors:

Helga Van HerleAuthors Info & Claims

Knowledge and Information Systems, Volume 11, Issue 1

Pages 1 - 27

Published: 01 January 2007 Publication History

Abstract

In this work we introduce the new problem of finding time seriesdiscords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While discords have many uses for data mining, they are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence) unlike most anomaly detection algorithms that typically require many parameters. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is three to four orders of magnitude faster than brute force, while guaranteed to produce identical results. We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms, space telemetry, respiration physiology, anthropological and video datasets.

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Fleischmann PHaschke LHuch AMayrock ANowotka D(2022)Nearly k-Universal Words - Investigating a Part of Simon’s CongruenceDescriptional Complexity of Formal Systems10.1007/978-3-031-13257-5_5(57-71)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-13257-5_5
Blázquez-García AConde AMori ULozano J(2021)A Review on Outlier/Anomaly Detection in Time Series DataACM Computing Surveys10.1145/344469054:3(1-33)Online publication date: 17-Apr-2021
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He JYeh CWu YWang LZhang W(2021)Mining Anomalies in Subspaces of High-Dimensional Time Series for Financial Transactional DataMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-030-86514-6_2(19-36)Online publication date: 13-Sep-2021
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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 11, Issue 1

January 2007

128 pages

ISSN:0219-1377

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Copyright © Copyright © 2007 Springer-Verlag London Limited.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2007

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Cited By

Fleischmann PHaschke LHuch AMayrock ANowotka D(2022)Nearly k-Universal Words - Investigating a Part of Simon’s CongruenceDescriptional Complexity of Formal Systems10.1007/978-3-031-13257-5_5(57-71)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-13257-5_5
Blázquez-García AConde AMori ULozano J(2021)A Review on Outlier/Anomaly Detection in Time Series DataACM Computing Surveys10.1145/344469054:3(1-33)Online publication date: 17-Apr-2021
https://dl.acm.org/doi/10.1145/3444690
He JYeh CWu YWang LZhang W(2021)Mining Anomalies in Subspaces of High-Dimensional Time Series for Financial Transactional DataMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-030-86514-6_2(19-36)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-86514-6_2
Wang TDuan LDong GBao Z(2020)Efficient Mining of Outlying Sequence Patterns for Analyzing Outlierness of Sequence DataACM Transactions on Knowledge Discovery from Data10.1145/339967114:5(1-26)Online publication date: 5-Aug-2020
https://dl.acm.org/doi/10.1145/3399671
Cao LYan YMadden SRundensteiner EGopalsamy M(2019)Efficient discovery of sequence outlier patternsProceedings of the VLDB Endowment10.14778/3324301.332430812:8(920-932)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.14778/3324301.3324308
Tang BTang HDong XJin BGe TCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)On Real-time Detecting Passenger Flow AnomaliesProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271754(1053-1062)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3271754
Lin QAdepu SVerwer SMathur AKim JAhn GKim SKim YLopez JKim T(2018)TABORProceedings of the 2018 on Asia Conference on Computer and Communications Security10.1145/3196494.3196546(525-536)Online publication date: 29-May-2018
https://dl.acm.org/doi/10.1145/3196494.3196546
Ozkan HOzkan FDelibalta IKozat S(2018)Efficient NP Tests for Anomaly Detection Over Birth-Death Type DTMCsJournal of Signal Processing Systems10.1007/s11265-016-1147-090:2(175-184)Online publication date: 1-Feb-2018
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Wang XLin JPatel NBraun M(2018)Exact variable-length anomaly detection algorithm for univariate and multivariate time seriesData Mining and Knowledge Discovery10.1007/s10618-018-0569-732:6(1806-1844)Online publication date: 1-Nov-2018
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