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Parametric methods for time series discrimination

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posted on 28.03.2022, 10:11 by Andrew Grant
In this thesis we consider the problem of determining whether two or more independent time series have been generated by the same underlying stochastic process, or by the same mechanism.There is an extensive literature on comparing time series from univariate stationary processes on the basis of their second order properties, that is, their dependence structures over time. These existing methods are nonparametric and are based on comparing periodograms or sample autocovariances. They are generally limited by requiring equal sample sizes and Gaussian assumptions. We introduce a parametric approach which involves fitting parametric models to the time series and comparing model parameters. The parametric approach avoids the limitations of the nonparametric and simulations are used to show that it results in a more powerful test. We also show how to extend the parametric approach to compare time series from multivariate stationary processes. A further extension is to compare time series which are from stochastic processes which contain periodic components. Such time series are typically modelled using mixed models which are made up of a deterministic periodic component and a stationary stochastic component. We develop tests for whether two or more time series have been generated by processes with periodicities at the same fixed frequencies and stationary components with the same second order properties. In order to extend the procedures to the multivariate case we first develop novel methods for frequency estimation in the multivariate mixed model.

History

Table of Contents

1. Introduction -- 2. Background -- 3. Autoregressive spectral discrimination -- 4. ARMA spectral discrimination -- 5. Comparing multivariate time series -- 6. The estimation of frequency in the multichannel sinusoidal model - 7. Discriminating between time series with periodic components -- 8. Conclusion -- References.

Notes

Empirical thesis. Bibliography: pages 195-199

Awarding Institution

Macquarie University

Degree Type

Thesis PhD

Degree

PhD, Macquarie University, Faculty of Science and Engineering, Department of Mathematics and Statistics

Department, Centre or School

Department of Mathematics and Statistics

Year of Award

2018

Principal Supervisor

Barry G. Quinn

Rights

Copyright Andrew Joseph Grant 2018. Copyright disclaimer: http://www.copyright.mq.edu.au

Language

English

Extent

1 online resource (xii, 199 pages) graphs, tables

Former Identifiers

mq:71295 http://hdl.handle.net/1959.14/1272839