Analysing time series structure with hidden Markov models

Mehdi Azzouzi, Ian T. Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper consides the problem of extracting the relationships between two time series in a non-linear non-stationary environment with Hidden Markov Models (HMMs). We describe an algorithm which is capable of identifying associations between variables. The method is applied both to synthetic data and real data. We show that HMMs are capable of modelling the oil drilling process and that they outperform existing methods.
Original languageEnglish
Title of host publicationProceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998
EditorsTony Constantinides, S. Y. Kung, Mahesan Niranjan, Elizabeth Wilson
Place of PublicationCambridge, UK
PublisherIEEE
Pages402-408
Number of pages7
Volume8
ISBN (Print)078035060
DOIs
Publication statusPublished - 2 Sep 1998
EventNeural Networks for Signal Processing -
Duration: 2 Sep 19982 Sep 1998

Publication series

NameProceedings of the 1998 IEEE Signal Processing Society Workshop
PublisherInstitute of Electrical and Electronics Engineers (IEEE)

Other

OtherNeural Networks for Signal Processing
Period2/09/982/09/98

Bibliographical note

©1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Keywords

  • non-linear
  • non-stationary environment
  • Hidden Markov Models
  • synthetic data
  • real data
  • oil drilling process

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  • Cite this

    Azzouzi, M., & Nabney, I. T. (1998). Analysing time series structure with hidden Markov models. In T. Constantinides, S. Y. Kung, M. Niranjan, & E. Wilson (Eds.), Proceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998 (Vol. 8, pp. 402-408). (Proceedings of the 1998 IEEE Signal Processing Society Workshop). IEEE. https://doi.org/10.1109/NNSP.1998.710670