Analyzing time series structure with Hidden Markov Models

Mehdi Azzouzi, Ian T. Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter 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 languageEnglish
Title of host publicationNeural Networks for Signal Processing - Proceedings of the IEEE Workshop
EditorsM. Niranjan, E. Wilson, T. Constantinides, S.Y. Kung
Place of PublicationUnited States
PublisherIEEE Computer Society
Pages402-408
Number of pages7
Publication statusPublished - 1 Jan 1998

Fingerprint Dive into the research topics of 'Analyzing time series structure with Hidden Markov Models'. Together they form a unique fingerprint.

  • Cite this

    Azzouzi, M., & Nabney, I. T. (1998). Analyzing time series structure with Hidden Markov Models. In M. Niranjan, E. Wilson, T. Constantinides, & S. Y. Kung (Eds.), Neural Networks for Signal Processing - Proceedings of the IEEE Workshop (pp. 402-408). IEEE Computer Society.