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Article

Online novelty detection on temporal sequences

Authors:

Simon PerkinsAuthors Info & Claims

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

Pages 613 - 618

https://doi.org/10.1145/956750.956828

Published: 24 August 2003 Publication History

Abstract

In this paper, we present a new framework for online novelty detection on temporal sequences. This framework include a mechanism for associating each detection result with a confidence value. Based on this framework, we develop a concrete online detection algorithm, by modeling the temporal sequence using an online support vector regression algorithm. Experiments on both synthetic and real world data are performed to demonstrate the promising performance of our proposed detection algorithm.

References

[1]

Bishop, C. M., Novelty Detection and Neural Network Validation. IEE Proceedings - Vision, Image and Signal Processing, vol. 141, no. 4, pp. 217--222, August, 1994.]]

[2]

Brotherton, Tom, Tom Johnson, and George Chadderdon, Classification and Novelty Detection Using Linear Models and a Class Dependent-Elliptical Basis Function Neural Network, in Proceedings of the International Conference on Neural Networks, Anchorage, May 1998.]]

[3]

Campbell, Colin, Kristin P. Bennett, A Linear Programming Approach to Novelty Detection, in Advances in Neural Information Processing Systems, vol 14, 2001.]]

[4]

Dasgupta, Dipanker, and Stephanie Forrest, Novelty Detection in Time Series Data Using Ideas from Immunology, In Proceedings of the 5th International Conference on Intelligent Systems, Reno, Nevada, June 19--21, 1996.]]

[5]

Guralnik, Valery, Jaideep Srivastava, Event Detection from Time Series Data. In Proceedings of the International Conference Knowledge Discovery and Data Mining, San Diego, California, 1999.]]

Digital Library

[6]

Isermann, Rolf, Process Fault Detection Based on Modeling and Estimation Method - A Survey, Automatica, vol. 20, pp. 387--404, 1984.]]

Digital Library

[7]

Jagadish, H. V., N. Kouda, and S. Muthukrishnan, Mining deviates in a time series database, in Proceedings of 25th International Conference on Very Large Data Bases, pp. 102--113, 1999.]]

Digital Library

[8]

Keogh, E., S Lonardi, and W Chiu, Finding Surprising Patterns in a Time Series Database In Linear Time and Space, In the 8th ACM SIGKDD International Conference on Kowledge Discovery and Data Mining, pp. 550--556, Edmonton, Alberta, Canada, July 23--26, 2002.]]

Digital Library

[9]

Kozma, R., M. Kitamura, M. Sakuma, and Y. Yokoyama, Anomaly Detection by Neural Network Models and Statistical Time Series Analysis, in Proceedings of IEEE International Conference on Neural Networks, Orlando, Florida, June 27--29, 1994.]]

[10]

Lauer, Martin, A Mixture Approach to Novelty Detection Using Training Data With Outliers, Lecture Notes in Computer Science, vol. 2167, pp. 300--310, 2001.]]

Digital Library

[11]

Ma, Junshui, James Theiler, and Simon Perkins, "Accurate Online Support Vector Regression," to appear in Neural Computation, 2003.]]

Digital Library

[12]

Mood, A. M., F. A. Graybill, and D. C. Boes, Introduction to the Thoery of Statistics, 3rd Edition, McGraw-Hill, Inc, 1974.]]

[13]

Roberts, S., and L. Tarassenko. A Probabilistic Resource Allocating Network for Novelty Detection, Neural Computation, vol. 6, pp. 270--284, 1994.]]

Digital Library

[14]

Schölkopf, B., R. C. Williamson, A. J. Smola, J. Shawe-Taylor, and J, Platt. Support vector method for novelty detection. In Neural Information Processing Systems, 2000.]]

[15]

Shahabi. C., X. Tian, and W. Zhao, Tsa-tree: A Wavelet-based Approach to Improve the Efficiency of Multi-level Surprise and Trend Queries. In Proceedings of 12th International Conference on Scientific and Scientific and Statistical Database Managment, 2000.]]

Digital Library

[16]

Smola, A. J., and B. Scholkopf (1998). A Tutorial on Support Vector Regression, NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK.]]

[17]

Ypma, Alexander, and Rober P. Duin, Novelty Detection Using Self-Organizing Maps, in Progress in Connectionist-Based Information Systems, pp. 1322--1325, London: Springer, 1997.]]

Cited By

Zamanzadeh Darban ZWebb GPan SAggarwal CSalehi M(2024)Deep Learning for Time Series Anomaly Detection: A SurveyACM Computing Surveys10.1145/369133857:1(1-42)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3691338
Dani SThakur CNagvanshi NSingh G(2024)Anomaly Detection using PCA in Time Series Data2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)10.1109/IATMSI60426.2024.10502929(1-6)Online publication date: 14-Mar-2024
https://doi.org/10.1109/IATMSI60426.2024.10502929
Nguyen NHeldal RPelliccione P(2024)Concept-drift-adaptive anomaly detector for marine sensor data streamsInternet of Things10.1016/j.iot.2024.10141428(101414)Online publication date: Dec-2024
https://doi.org/10.1016/j.iot.2024.101414
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Index Terms

Online novelty detection on temporal sequences
1. Computing methodologies
  1. Machine learning

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Information

Published In

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2003

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KDD03

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

August 24 - 27, 2003

Washington, D.C.

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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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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

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Cited By

Zamanzadeh Darban ZWebb GPan SAggarwal CSalehi M(2024)Deep Learning for Time Series Anomaly Detection: A SurveyACM Computing Surveys10.1145/369133857:1(1-42)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3691338
Dani SThakur CNagvanshi NSingh G(2024)Anomaly Detection using PCA in Time Series Data2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)10.1109/IATMSI60426.2024.10502929(1-6)Online publication date: 14-Mar-2024
https://doi.org/10.1109/IATMSI60426.2024.10502929
Nguyen NHeldal RPelliccione P(2024)Concept-drift-adaptive anomaly detector for marine sensor data streamsInternet of Things10.1016/j.iot.2024.10141428(101414)Online publication date: Dec-2024
https://doi.org/10.1016/j.iot.2024.101414
Cao FGuo X(2024)Automated financial time series anomaly detection via curiosity-guided exploration and self-imitation learningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108663135:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108663
Wordliczek Ł(2023)Bridging Disciplinary Divides: Exploring the Synergy of Punctuated Equilibrium Theory and Artificial Neural Networks in Policy Change AnalysisBarometr Regionalny. Analizy i Prognozy10.56583/br.219119:2(195-212)Online publication date: 31-Dec-2023
https://doi.org/10.56583/br.2191
Agrahari SSrivastava SSingh A(2023)Review on novelty detection in the non-stationary environmentKnowledge and Information Systems10.1007/s10115-023-02018-x66:3(1549-1574)Online publication date: 30-Nov-2023
https://doi.org/10.1007/s10115-023-02018-x
He JCheng ZGuo B(2022)Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based MethodSensors10.3390/s2217635822:17(6358)Online publication date: 24-Aug-2022
https://doi.org/10.3390/s22176358
Sabeti EOh SSong PHero A(2022)A Pattern Dictionary Method for Anomaly DetectionEntropy10.3390/e2408109524:8(1095)Online publication date: 9-Aug-2022
https://doi.org/10.3390/e24081095
Schmidl SWenig PPapenbrock T(2022)Anomaly detection in time seriesProceedings of the VLDB Endowment10.14778/3538598.353860215:9(1779-1797)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.14778/3538598.3538602
Huang CMin GWu YYing YPei KXiang Z(2022)Time Series Anomaly Detection for Trustworthy Services in Cloud Computing SystemsIEEE Transactions on Big Data10.1109/TBDATA.2017.27110398:1(60-72)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TBDATA.2017.2711039
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