Modelling complex biologging data with hidden Markov models
department or school: Science, Faculty of
department or school: Statistics, Department of
data provider: DSpace
degree campus: UBCV
creator: Sidrow, Evan
issued: 2024-10-17T19:29:54Z
issued: 2024
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Hidden Markov models (HMMs) are commonly used to identify latent processes from observed time series, but it is challenging to fit them to large and complex time series collected by modern sensors. Using data from threatened resident killer whales (Orcinus orca) as a case study, this dissertation provides solutions to three common challenges when identifying latent behaviour from biologging data. First, time series are now collected at high frequencies and exhibit fine-scale dependence structures that violate common assumptions of HMMs. I propose a hierarchical approach that divides time series into sequences of curves. A coarse-scale HMM models the sequence of curves while a related fine-scale HMM describes the structure within each curve. The fine-scale model includes autoregression to capture dependence in addition to moving-window summaries and Fourier analysis to capture intricate fine-scale structures. I use simulation and case studies to show that this framework produces more interpretable state estimation and more accurate parameter estimates than existing methods. Second, many modern biologging time series include labels of the latent process of interest. Labels can improve predictive accuracy, but sparse labels can have a negligible influence on parameter estimates. I introduce a weighted likelihood approach that increases the relative influence of labeled observations. I use this approach to develop two HMMs that model the foraging behaviour of killer whales at different scales. Using cross-validated evaluation metrics, I show that my weighted method produces more accurate and understandable models than unweighted methods. Finally, applying HMMs to very large time series is computationally demanding, as expectation-maximization (EM) algorithms typically iterate through the entire data set for every parameter update. I propose a novel optimization algorithm that combines a partial E step with variance-reduced stochastic optimization within the M step. I pr…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89430
note: Modelling Complex Biologging Datawith Hidden Markov ModelsbyEvan SidrowB.Sc., The University of Colorado Boulder, 2018M.Sc., The University of Colorado Boulder, 2018A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Statistics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)October 2024© Evan Sidrow 2024The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the dissertation entitled:Modelling Complex Biologging Data with Hidden Markov Modelssubmitted by Evan Sidrow in partial fulfillment of the requirements for the degree ofDoctor of Philosophy in Statistics.Examining Committee:Nancy Heckman, Professor Emeritus, Department of Statistics, UBCCo-supervisorMarie Auger-Méthé, Associate Professor, Department of Statistics, UBCCo-supervisorAlexandre Bouchard-Côté, Professor, Department of Statistics, UBCSupervisory Committee MemberMatías Saibián-Barrera, Professor, Department of Statistics, UBCUniversity ExaminerDaniel Coombs, Professor, Department of Mathematics, UBCUniversity ExaminerRoland Langrock, Professor, Statistics and Data Analysis Group, Bielefeld UniversityExternal ExaminerAdditional Supervisory Committee Members:Jim Zidek, Professor Emeritus, Department of Statistics, UBCSupervisory Committee MemberiiAbstractHidden Markov models (HMMs) are commonly used to identify latent processes from ob-served time series, but it is challenging to fit them to large and complex time series collectedby modern sensors. Using data from threatened resident killer whales (Orcinus orca) asa case study, this dissertation provides solutions to three common challenges when iden-tifying latent behaviour from biologging data. First, time series are now collected at highfrequencies and exhibit fine-scale dependence structures that violate common assumptionsof HMMs. I propose a hierarchical approach that div…
has type: Thesis/Dissertation
date issued: 2024-11
is shown at: 10.14288/1.0445591
language: eng
degree discipline: Statistics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Heckman, Nancy
contributor: Auger-Méthé, Marie
type: Text
identifier URI: http://hdl.handle.net/2429/89430