Abstract
Original language | English |
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Place of Publication | Aston University, Birmingham, UK |
Publisher | Aston University |
Number of pages | 18 |
ISBN (Print) | NCRG/98/026 |
Publication status | Published - 1998 |
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Bibliographical note
Submitted to Pattern Analysis and Machine IntelligenceKeywords
- lag detection
- hidden Markov models
- non-stationarity
- regime switching
- EM algorithm
- mutual information
Cite this
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Delay estimation for multivariate time series. / Azzouzi, Mehdi; Nabney, Ian T.
Aston University, Birmingham, UK : Aston University, 1998.Research output: Working paper › Technical report
TY - UNPB
T1 - Delay estimation for multivariate time series
AU - Azzouzi, Mehdi
AU - Nabney, Ian T.
N1 - Submitted to Pattern Analysis and Machine Intelligence
PY - 1998
Y1 - 1998
N2 - 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 (HMMs) to identify the lag (or delay) between different variables for such data. We first present a method using maximum likelihood estimation and propose a simple algorithm which is capable of identifying associations between variables. We also adopt an information-theoretic approach and develop a novel procedure for training HMMs to maximise the mutual information between delayed time series. Both methods are successfully applied to real data. We model the oil drilling process with HMMs and estimate a crucial parameter, namely the lag for return.
AB - 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 (HMMs) to identify the lag (or delay) between different variables for such data. We first present a method using maximum likelihood estimation and propose a simple algorithm which is capable of identifying associations between variables. We also adopt an information-theoretic approach and develop a novel procedure for training HMMs to maximise the mutual information between delayed time series. Both methods are successfully applied to real data. We model the oil drilling process with HMMs and estimate a crucial parameter, namely the lag for return.
KW - lag detection
KW - hidden Markov models
KW - non-stationarity
KW - regime switching
KW - EM algorithm
KW - mutual information
M3 - Technical report
SN - NCRG/98/026
BT - Delay estimation for multivariate time series
PB - Aston University
CY - Aston University, Birmingham, UK
ER -