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

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

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

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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.]]

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Cited By

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
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
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Index Terms

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

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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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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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Cited By

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
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
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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
Hung PAnh D(2022)Improving River Runoff Forecasting through Anomaly Detection and Repair2022 RIVF International Conference on Computing and Communication Technologies (RIVF)10.1109/RIVF55975.2022.10013913(250-255)Online publication date: 20-Dec-2022
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de Riberolles TZou YSilvestre GLochin ESong J(2022)Anomaly detection for ICS based on deep learning: a use case for aeronautical radar dataAnnals of Telecommunications10.1007/s12243-021-00902-777:11-12(749-761)Online publication date: 28-Jan-2022
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Bruto da Costa ADasgupta P(2021)Learning Temporal Causal Sequence Relationships from Real-Time Time-SeriesJournal of Artificial Intelligence Research10.1613/jair.1.1239570(205-243)Online publication date: 1-May-2021
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