Analysing time series structure with hidden Markov models

Mehdi Azzouzi, Ian T. Nabney

Research output: Chapter in Book/Published conference outputChapter


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
Number of pages7
ISBN (Print)078035060
Publication statusPublished - 2 Sept 1998
EventNeural Networks for Signal Processing -
Duration: 2 Sept 19982 Sept 1998

Publication series

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


OtherNeural Networks for Signal Processing

Bibliographical note

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


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