Analyzing time series structure with Hidden Markov Models

Mehdi Azzouzi; Ian T. Nabney

Analyzing time series structure with Hidden Markov Models

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

Abstract

This paper considers 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 language	English
Title of host publication	Neural Networks for Signal Processing - Proceedings of the IEEE Workshop
Editors	M. Niranjan, E. Wilson, T. Constantinides, S.Y. Kung
Place of Publication	United States
Publisher	IEEE Computer Society
Pages	402-408
Number of pages	7
Publication status	Published - 1 Jan 1998

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abstract = "This paper considers 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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AB - This paper considers 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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BT - Neural Networks for Signal Processing - Proceedings of the IEEE Workshop

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A2 - Wilson, E.

A2 - Constantinides, T.

A2 - Kung, S.Y.

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