Analysing time series structure with hidden Markov models

Mehdi Azzouzi; Ian T. Nabney

doi:10.1109/NNSP.1998.710670

Analysing time series structure with hidden Markov models

Mehdi Azzouzi
, Ian T. Nabney

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

14 Citations (Scopus)

11 Downloads (Pure)

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.

Original language	English
Title of host publication	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	United States
Publisher	IEEE Computer Society
Pages	402-408
Number of pages	7
Volume	8
ISBN (Print)	078035060
DOIs	https://doi.org/10.1109/NNSP.1998.710670
Publication status	Published - 1 Sept 1998

Publication series

Name	Proceedings of the 1998 IEEE Signal Processing Society Workshop
Publisher	IEEE

Bibliographical 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

Access to Document

10.1109/NNSP.1998.710670

Handle.net

Persistent link

Cite this

Azzouzi, M., & Nabney, I. T. (1998). Analysing time series structure with hidden Markov models. In T. Constantinides, S. Y. Kung, M. Niranjan, & E. Wilson (Eds.), Proceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998 (Vol. 8, pp. 402-408). (Proceedings of the 1998 IEEE Signal Processing Society Workshop). IEEE Computer Society. https://doi.org/10.1109/NNSP.1998.710670

Azzouzi, Mehdi ; Nabney, Ian T. / Analysing time series structure with hidden Markov models. Proceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998. editor / Tony Constantinides ; S. Y. Kung ; Mahesan Niranjan ; Elizabeth Wilson. Vol. 8 United States : IEEE Computer Society, 1998. pp. 402-408 (Proceedings of the 1998 IEEE Signal Processing Society Workshop).

@inbook{b44e693e72054aad9af1b1876fc381bf,

title = "Analysing time series structure with hidden Markov models",

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.",

keywords = "non-linear, non-stationary environment, Hidden Markov Models, synthetic data, real data, oil drilling process",

author = "Mehdi Azzouzi and Nabney, \{Ian T.\}",

note = "{\textcopyright}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.",

year = "1998",

month = sep,

day = "1",

doi = "10.1109/NNSP.1998.710670",

language = "English",

isbn = "078035060",

volume = "8",

series = "Proceedings of the 1998 IEEE Signal Processing Society Workshop",

publisher = "IEEE Computer Society",

pages = "402--408",

editor = "Tony Constantinides and Kung, \{S. Y.\} and Mahesan Niranjan and Elizabeth Wilson",

booktitle = "Proceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998",

address = "United States",

}

Azzouzi, M & Nabney, IT 1998, Analysing time series structure with hidden Markov models. in T Constantinides, SY Kung, M Niranjan & E Wilson (eds), Proceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998. vol. 8, Proceedings of the 1998 IEEE Signal Processing Society Workshop, IEEE Computer Society, United States, pp. 402-408. https://doi.org/10.1109/NNSP.1998.710670

Analysing time series structure with hidden Markov models. / Azzouzi, Mehdi; Nabney, Ian T.
Proceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998. ed. / Tony Constantinides; S. Y. Kung; Mahesan Niranjan; Elizabeth Wilson. Vol. 8 United States: IEEE Computer Society, 1998. p. 402-408 (Proceedings of the 1998 IEEE Signal Processing Society Workshop).

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

TY - CHAP

T1 - Analysing time series structure with hidden Markov models

AU - Azzouzi, Mehdi

AU - Nabney, Ian T.

N1 - ©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.

PY - 1998/9/1

Y1 - 1998/9/1

N2 - 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.

AB - 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.

KW - non-linear, non-stationary environment, Hidden Markov Models, synthetic data, real data, oil drilling process

U2 - 10.1109/NNSP.1998.710670

DO - 10.1109/NNSP.1998.710670

M3 - Chapter in a book

SN - 078035060

VL - 8

T3 - Proceedings of the 1998 IEEE Signal Processing Society Workshop

SP - 402

EP - 408

BT - Proceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998

A2 - Constantinides, Tony

A2 - Kung, S. Y.

A2 - Niranjan, Mahesan

A2 - Wilson, Elizabeth

PB - IEEE Computer Society

CY - United States

ER -

Azzouzi M, Nabney IT. Analysing time series structure with hidden Markov models. In Constantinides T, Kung SY, Niranjan M, Wilson E, editors, Proceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998. Vol. 8. United States: IEEE Computer Society. 1998. p. 402-408. (Proceedings of the 1998 IEEE Signal Processing Society Workshop). doi: 10.1109/NNSP.1998.710670

Analysing time series structure with hidden Markov models

Abstract

Publication series

Bibliographical note

Keywords

Access to Document

Handle.net

Fingerprint

Cite this