Time delay estimation with hidden Markov models


Most traditional methods for extracting the relationships between two time series are based on cross-correlation. In a non-linear non-stationary environment, these techniques are not sufficient. We show in this paper how to use hidden Markov models to identify the lag (or delay) between different variables for such data. Adopting an information-theoretic approach, we develop a procedure for training HMMs to maximise the mutual information (MMI) between delayed time series. The method is used to model the oil drilling process. We show that cross-correlation gives no information and that the MMI approach outperforms maximum likelihood.

Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: ©1999 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: Ninth International conference on Artificial Neural Networks
Event Type: Other
Event Dates: 1999-01-01 - 1999-01-01
Uncontrolled Keywords: time series,cross-correlation,non-stationary,Markov models,information-theoretic,mutual information
ISBN: 0852967217
Last Modified: 04 Jul 2024 07:31
Date Deposited: 15 Sep 2009 15:20
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://ieeexplo ... &isnumber=17760 (Publisher URL)
PURE Output Type: Chapter
Published Date: 1999
Authors: Azzouzi, Mehdi
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)



Version: Published Version

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