Delay estimation for multivariate time series


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.

Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: Submitted to Pattern Analysis and Machine Intelligence
Uncontrolled Keywords: lag detection,hidden Markov models,non-stationarity,regime switching,EM algorithm,mutual information
ISBN: NCRG/98/026
Last Modified: 15 Apr 2024 07:49
Date Deposited: 11 Mar 2019 17:21
PURE Output Type: Technical report
Published Date: 1998
Authors: Azzouzi, Mehdi
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)

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