›› 2013, Vol. 28 ›› Issue (2): 300-310.doi: 10.1007/s11390-013-1330-8

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

Parameter-Free Search of Time-Series Discord

Wei Luo1, Marcus Gallagher2, Member, IEEE, and Janet Wiles2, Member, IEEE   

  1. 1 School of Information Technology, Deakin University, Geelong, VIC 3220, Australia;
    2 School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD 4072, Australia
  • Received:2012-02-15 Revised:2012-11-14 Online:2013-03-05 Published:2013-03-05
  • Supported by:

    Support by Australian Research Council Linkage Grant No. LP 0776417.

Time-series discord is widely used in data mining applications to characterize anomalous subsequences in time series. Compared to some other discord search algorithms, the direct search algorithm based on the recurrence plot shows the advantage of being fast and parameter free. The direct search algorithm, however, relies on quasi-periodicity in input time series, an assumption that limits the algorithm's applicability. In this paper, we eliminate the periodicity assumption from the direct search algorithm by proposing a reference function for subsequences and a new sampling strategy based on the reference function. These measures result in a new algorithm with improved efficiency and robustness, as evidenced by our empirical evaluation.

[1] Shoeb A, Edwards H, Connolly J et al. Patient-specificseizure onset detection. Epilepsy & Behavior, 2004, 5(4): 483-498.

[2] Febrero M, Galeano P, González-Manteiga W. A functionalanalysis of NOx levels: Location and scale estimation andoutlier detection. Computational Statistics, 2007, 22(3): 411-427.

[3] van Wijk J, Van Selow E. Cluster and calendar based visual-ization of time series data. In Proc. the 1999 IEEE Sympo-sium on Information Visualization, Oct. 1999, pp.4-9.

[4] Rebbapragada U, Protopapas P, Brodley C, Alcock C. Find-ing anomalous periodic time series. Machine learning, 2009,74(3): 281-313.

[5] Rousseeuw P, Leroy A. Robust Regression and Outlier Detec-tion. Wiley Online Library, 1987.

[6] Dasgupta D, Forrest S. Novelty detection in time series datausing ideas from immunology. In Proc. the InternationalConference on Intelligent Systems, Dec. 1996, pp.82-87.

[7] Hyndman R, Ullah S. Robust forecasting of mortality andfertility rates: A functional data approach. ComputationalStatistics & Data Analysis, 2007, 51(10): 4942-4956.

[8] Febrero M, Galeano P, González-Manteiga W. Outlier detec-tion in functional data by depth measures, with application toidentify abnormal NOX levels. Environmetrics, 2008, 19(4):331-345.

[9] Keogh E, Lin J, Fu A. HOT SAX: Efficiently finding the mostunusual time series subsequence. In Proc. the 5th IEEE In-ternational Conference on Data Mining, Nov. 2005, pp.226-233.

[10] Lin J, Keogh E, Fu A, Van Herle H. Approximations to magic:Finding unusual medical time series. In Proc. the 18th IEEESymposium on Computer-Based Medical Systems, Jun. 2005,pp.329-334.

[11] Bu Y, Leung T, Fu A, Keogh E, Pei J, Meshkin S. WAT:Finding top-K discords in time series database. In Proc. the7th SIAM International Conference on Data Mining, Apr.2007.

[12] Yankov D, Keogh E, Rebbapragada U. Disk aware discord dis-covery: Finding unusual time series in terabyte sized datasets.Knowledge and Information Systems, 2008, 17(2): 241-262.

[13] Goldberger A, Amaral L, Glass L et al. Physiobank, phys-iotoolkit, and physionet: Components of a new research re-source for complex physiologic signals. Circulation, 2000,101(23): e215-e220.

[14] Shoeb A, Guttag J. Application of machine learning to epilep-tic seizure detection. In Proc. the 27th International Confer-ence on Machine Learning, Jun. 2010, pp.975-982.

[15] Pham N, Le Q, Dang T. HOT aSAX: A novel adaptive sym-bolic representation for time series discords discovery. InProc. the 2nd Intelligent Information and Database Systems,Mar. 2010, pp.113-121.

[16] Fu A, Leung O, Keogh E, Lin J. Finding time series discordsbased on Haar transform. In Proc. the 2nd Advanced DataMining and Applications, Aug. 2006, pp.31-41.

[17] Shieh J, Keogh E. iSAX: Indexing and mining terabyte sizedtime series. In Proc. the 14th ACM SIGKDD InternationalConference on Knowledge Discovery and Data Mining, Aug.2008, pp.623-631.

[18] Son M, Anh D. EWAT+: Finding time series discords basedon new discord measure functions. In Proc. IEEE Inter-national Conference on Computing & Communication Tech-nologies, Research, Innovation, and Vision for the Future,Nov. 2010, pp.1-4.

[19] Luo W, Gallagher M. Faster and parameter-free discordsearch in quasi-periodic time series. In Proc. PAKDD, May2011, pp.135-148.

[20] Eckmann J, Kamphorst S, Ruelle D. Recurrence plots of dy-namical systems. Europhysics Letters, 1987, 4(9): 973-977.

[21] Iwanski J, Bradley E. Recurrence plots of experimental data:To embed or not to embed? Chaos: An InterdisciplinaryJournal of Nonlinear Science, 1998, 8(4): 861-871.

[22] Webber Jr C, Zbilut J. Recurrence quantification analysisof nonlinear dynamical systems. Tutorials in ContemporaryNonlinear Methods for the Behavioral Sciences, 2005, pp.26-94.

[23] Marwan N, Carmen Romano M C, Thiel M, Kurths J. Re-currence plots for the analysis of complex systems. PhysicsReports, 2007, 438(5/6): 237-329.

[24] Marwan N. A historical review of recurrence plots. The Eu-ropean Physical Journal-Special Topics, 2008, 164(1): 3-12.

[25] Chatfield C. The Analysis of Time Series: An Introduction,(6th edition). Chapman & Hall/CRC, 2004.

[26] Keogh E, Lin J, Fu A. The UCR time series discords.http://www.cs.ucr.edu/~eamonn/discords/, April 2010.

[27] Hyndman R. Time series data library. http://data.is/TSDLde-mo, April 2010.
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[1] Liu Mingye; Hong Enyu;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[2] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[3] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[4] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[5] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] Min Yinghua; Han Zhide;. A Built-in Test Pattern Generator[J]. , 1986, 1(4): 62 -74 .
[7] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[8] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[9] Zhu Hong;. Some Mathematical Properties of the Functional Programming Language FP[J]. , 1987, 2(3): 202 -216 .
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