Wavelet-based space partitioning for symbolic time series analysis

Venkatesh Rajagopalan, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Recent literature has reported symbolic time series analysis of complex systems for real-time anomaly detection. A crucial aspect in this analysis is symbol sequence generation from the observed time series data. This paper presents a wavelet-based partitioning, instead of the currently practiced method of phase-space partitioning, for symbol generation. The partitioning algorithm makes use of the maximum entropy method. The wavelet-space and phase-space partitioning methods are compared with regard to anomaly detection using experimental data.

Original languageEnglish (US)
Title of host publicationProceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05
Pages5245-5250
Number of pages6
DOIs
StatePublished - Dec 1 2005
Event44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05 - Seville, Spain
Duration: Dec 12 2005Dec 15 2005

Publication series

NameProceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05
Volume2005

Other

Other44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05
CountrySpain
CitySeville
Period12/12/0512/15/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Rajagopalan, V., & Ray, A. (2005). Wavelet-based space partitioning for symbolic time series analysis. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05 (pp. 5245-5250). [1582995] (Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05; Vol. 2005). https://doi.org/10.1109/CDC.2005.1582995