Analysing time series structure with hidden Markov models


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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Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: ©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.
Event Title: Neural Networks for Signal Processing
Event Type: Other
Event Dates: 1998-09-02 - 1998-09-02
Uncontrolled Keywords: non-linear,non-stationary environment,Hidden Markov Models,synthetic data,real data,oil drilling process
ISBN: 078035060
Last Modified: 27 Jun 2024 12:04
Date Deposited: 17 Sep 2009 09:16
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Related URLs: http://ieeexplo ... &isnumber=15338 (Publisher URL)
PURE Output Type: Chapter
Published Date: 1998-09-02
Authors: Azzouzi, Mehdi
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)



Version: Published Version

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