article

Multi-scale anomaly detection algorithm based on infrequent pattern of time series

Authors:

Yan-yan ZhanAuthors Info & Claims

Journal of Computational and Applied Mathematics, Volume 214, Issue 1

Pages 227 - 237

https://doi.org/10.1016/j.cam.2007.02.027

Published: 01 April 2008 Publication History

Abstract

In this paper, we propose two anomaly detection algorithms PAV and MPAV on time series. The first basic idea of this paper defines that the anomaly pattern is the most infrequent time series pattern, which is the lowest support pattern. The second basic idea of this paper is that PAV detects directly anomalies in the original time series, and MPAV algorithm extraction anomaly in the wavelet approximation coefficient of the time series. For complexity analyses, as the wavelet transform have the functions to compress data, filter noise, and maintain the basic form of time series, the MPAV algorithm, while maintaining the accuracy of the algorithm improves the efficiency. As PAV and MPAV algorithms are simple and easy to realize without training, this proposed multi-scale anomaly detection algorithm based on infrequent pattern of time series can therefore be proved to be very useful for computer science applications.

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Cited By

Zhou YRen HZhao DLi ZPedrycz W(2022)A novel multi-level framework for anomaly detection in time series dataApplied Intelligence10.1007/s10489-022-04016-y53:9(10009-10026)Online publication date: 13-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-022-04016-y
Horváth GKovács EMolontay RNováczki S(2020)Copula-Based Anomaly Scoring and Localization for Large-Scale, High-Dimensional Continuous DataACM Transactions on Intelligent Systems and Technology10.1145/337227411:3(1-26)Online publication date: 17-Apr-2020
https://dl.acm.org/doi/10.1145/3372274
Iegorov OFischmeister S(2020)Parameterless Semi-supervised Anomaly Detection in Univariate Time SeriesMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-67658-2_37(644-659)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-67658-2_37
Show More Cited By

Index Terms

Multi-scale anomaly detection algorithm based on infrequent pattern of time series
1. Computing methodologies
  1. Machine learning
  2. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
      1. Linear algebra algorithms
2. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
      1. Computation of transforms
      2. Computations on matrices
  2. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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Copyright © Elsevier B.V. © 2007.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 April 2008

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Cited By

Zhou YRen HZhao DLi ZPedrycz W(2022)A novel multi-level framework for anomaly detection in time series dataApplied Intelligence10.1007/s10489-022-04016-y53:9(10009-10026)Online publication date: 13-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-022-04016-y
Horváth GKovács EMolontay RNováczki S(2020)Copula-Based Anomaly Scoring and Localization for Large-Scale, High-Dimensional Continuous DataACM Transactions on Intelligent Systems and Technology10.1145/337227411:3(1-26)Online publication date: 17-Apr-2020
https://dl.acm.org/doi/10.1145/3372274
Iegorov OFischmeister S(2020)Parameterless Semi-supervised Anomaly Detection in Univariate Time SeriesMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-67658-2_37(644-659)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-67658-2_37
Feremans LVercruyssen VCule BMeert WGoethals B(2019)Pattern-Based Anomaly Detection in Mixed-Type Time SeriesMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-46150-8_15(240-256)Online publication date: 16-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-46150-8_15
Ren HLiao XLi ZAi-Ahmari A(2018)Anomaly detection using piecewise aggregate approximation in the amplitude domainApplied Intelligence10.1007/s10489-017-1017-x48:5(1097-1110)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10489-017-1017-x
Ren HLiu MLi ZPedrycz W(2017)A Piecewise Aggregate pattern representation approach for anomaly detection in time seriesKnowledge-Based Systems10.1016/j.knosys.2017.07.021135:C(29-39)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.knosys.2017.07.021
Kanarachos SChristopoulos SChroneos AFitzpatrick M(2017)Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transformExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.04.02885:C(292-304)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.eswa.2017.04.028
Moreno-Garcia AMoreno-Garcia JJimenez-Linares LRodriguez-Benitez L(2017)Time series represented by means of fuzzy piecewise lineal segmentsJournal of Computational and Applied Mathematics10.1016/j.cam.2016.11.003318:C(156-167)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.cam.2016.11.003
Esling PAgon C(2012)Time-series data miningACM Computing Surveys10.1145/2379776.237978845:1(1-34)Online publication date: 7-Dec-2012
https://dl.acm.org/doi/10.1145/2379776.2379788
Ando SSuzuki EElder JFogelman FFlach PZaki M(2009)Detection of unique temporal segments by information theoretic meta-clusteringProceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1557019.1557033(59-68)Online publication date: 28-Jun-2009
https://dl.acm.org/doi/10.1145/1557019.1557033

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