Analysing time series structure with hidden Markov models

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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.

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  • 1998_IEEE_Signal_Processing_Society.pdf

    Rights statement: ©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.

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Details

Publication date2 Sep 1998
Publication titleProceedings 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
Original languageEnglish
EventNeural Networks for Signal Processing -

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

Bibliographic 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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