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Bayesian change-point modeling with segmented ARMA model

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thesis
posted on 26.05.2020, 22:35 authored by FARHANA SADIA
Time series segmentation is a dynamic sphere of research that has attracted attention in diverse topics. The aim of this thesis is to segment time series data using a Bayesian approach and develop methods to segment multiple parallel time series data. This thesis used a novel methodology called the Bayesian change-point segmented ARMA model, which estimates the change-point locations by segmenting a time series using an ARMA model. It also uses a highly efficient technique (Generalized Gibbs Sampler) to generate samples from a posterior distribution. Three alternative generalizations of the Bayesian change-point segmented ARMA model were developed to segment multiple parallel time series data.

History

Principal supervisor

Jonathan Macgregor Keith

Additional supervisor 1

Robert Bryson-Richardson

Additional supervisor 2

Kate Smith-Miles

Year of Award

2020

Department, School or Centre

Mathematics

Course

Doctor of Philosophy

Degree Type

DOCTORATE