Article

Probabilistic discovery of time series motifs

Authors:

Stefano LonardiAuthors Info & Claims

KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 493 - 498

https://doi.org/10.1145/956750.956808

Published: 24 August 2003 Publication History

Abstract

Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of this work were the poor scalability of the motif discovery algorithm, and the inability to discover motifs in the presence of noise.Here we address these limitations by introducing a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences. Our algorithm is probabilistic in nature, but as we show empirically and theoretically, it can find time series motifs with very high probability even in the presence of noise or "don't care" symbols. Not only is the algorithm fast, but it is an anytime algorithm, producing likely candidate motifs almost immediately, and gradually improving the quality of results over time.

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Cited By

Komitova RMemmert D(2024)Time Series Data MiningComputer Science in Sport10.1007/978-3-662-68313-2_17(141-148)Online publication date: 6-Mar-2024
https://doi.org/10.1007/978-3-662-68313-2_17
Moshkin VKurilo DYarushkina N(2023)Integration of Fuzzy Ontologies and Neural Networks in the Detection of Time Series AnomaliesMathematics10.3390/math1105120411:5(1204)Online publication date: 1-Mar-2023
https://doi.org/10.3390/math11051204
Paparrizos JWu KElmore AFaloutsos CFranklin M(2023)Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding DistancesProceedings of the VLDB Endowment10.14778/3594512.359453016:8(2019-2032)Online publication date: 22-Jun-2023
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Index Terms

Probabilistic discovery of time series motifs
1. Information systems
  1. Information systems applications

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cover image ACM Conferences

KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2003

736 pages

ISBN:1581137370

DOI:10.1145/956750

Conference Chair:
Lise Getoor
University of Maryland, College Park
,
General Chair:
Ted Senator
DARPA
,
Program Chairs:
Pedro Domingos
University of Washington
,
Christos Faloutsos
Carnegie Mellon University

Copyright © 2003 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 August 2003

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KDD03: The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2003

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KDD '03 Paper Acceptance Rate 46 of 298 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Komitova RMemmert D(2024)Time Series Data MiningComputer Science in Sport10.1007/978-3-662-68313-2_17(141-148)Online publication date: 6-Mar-2024
https://doi.org/10.1007/978-3-662-68313-2_17
Moshkin VKurilo DYarushkina N(2023)Integration of Fuzzy Ontologies and Neural Networks in the Detection of Time Series AnomaliesMathematics10.3390/math1105120411:5(1204)Online publication date: 1-Mar-2023
https://doi.org/10.3390/math11051204
Paparrizos JWu KElmore AFaloutsos CFranklin M(2023)Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding DistancesProceedings of the VLDB Endowment10.14778/3594512.359453016:8(2019-2032)Online publication date: 22-Jun-2023
https://dl.acm.org/doi/10.14778/3594512.3594530
Zarei RHuang GWu J(2023)GraphTS: Graph-represented time series for subsequence anomaly detectionPLOS ONE10.1371/journal.pone.029009218:8(e0290092)Online publication date: 16-Aug-2023
https://doi.org/10.1371/journal.pone.0290092
Imamura MNakamura TSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Parameter-free Spikelet: Discovering Different Length and Warped Time Series Motifs using an Adaptive Time Series RepresentationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599310(857-866)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599310
Yuan RPourmousavi SSoong WNguyen GLiisberg J(2023)IRMAC: Interpretable Refined Motifs in Binary Classification for smart grid applicationsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105588117(105588)Online publication date: Jan-2023
https://doi.org/10.1016/j.engappai.2022.105588
Chao ZGao HMiao DWang H(2023)Discovering time series motifs of all lengths using dynamic time warpingWorld Wide Web10.1007/s11280-023-01207-626:6(3815-3836)Online publication date: 20-Sep-2023
https://doi.org/10.1007/s11280-023-01207-6
Quislant RFernandez IGutierrez EPlata O(2023)Time series analysis acceleration with advanced vectorization extensionsThe Journal of Supercomputing10.1007/s11227-023-05060-279:9(10178-10207)Online publication date: 2-Feb-2023
https://doi.org/10.1007/s11227-023-05060-2
Komitova RMemmert D(2023)Zeitreihen Data MiningSportinformatik10.1007/978-3-662-67026-2_17(159-167)Online publication date: 15-Oct-2023
https://doi.org/10.1007/978-3-662-67026-2_17
Zymbler MGoglachev A(2022)Fast Summarization of Long Time Series with Graphics ProcessorMathematics10.3390/math1010178110:10(1781)Online publication date: 23-May-2022
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