Analysing time series structure with hidden Markov models

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Authors

Mehdi Azzouzi
Ian T. NabneyORCiD: http://orcid.org/0000-0003-1513-993X

Research units

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.

Documents

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.
403 KB, PDF-document

Details

Publication date	2 Sep 1998
Publication title	Proceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998
Editors	Tony Constantinides, S. Y. Kung, Mahesan Niranjan, Elizabeth Wilson
Place of Publication	Cambridge, UK
Publisher	IEEE
Pages	402-408
Number of pages	7
Volume	8
ISBN (Print)	078035060
Original language	English
Event	Neural Networks for Signal Processing -

Publication series

Name	Proceedings of the 1998 IEEE Signal Processing Society Workshop
Publisher	Institute of Electrical and Electronics Engineers (IEEE)

Other

Other	Neural Networks for Signal Processing
Period	2/09/98 → 2/09/98

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

DOI

10.1109/NNSP.1998.710670
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