Clustering wildfire occurrence time series
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Snyman, Simon
issued: 2025-09-02T16:27:25Z
issued: 2025
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Exponential smoothing methods offer several tools for forecasting and simulating varying time series patterns, including historical wildfire counts. The Box-Cox transform, ARMA errors, trend and seasonal components (BATS), and the trigonometric BATS model (TBATS) are exponential smoothing techniques capable of modeling complex seasonality, non-integer frequencies, and more. The BATS and TBATS frameworks support short- and intermediate-term forecasting and simulation, while also providing a foundation for model-based time series clustering of wildfire counts. The application in this thesis of the BATS and TBATS models aims to support fire agencies in forecasting and simulating wildfire occurrences across Canada on an increased scale that provides alternative methods for future use. Variable length Markov chains (VLMCs) are an extension of traditional fixed-order Markov chains that can reduce model complexity and improve computational efficiency. To assess the practicality and effectiveness of VLMCs in modeling wildfire causes in Canada, they are evaluated in terms of forecasting on both balanced and unbalanced data. Furthermore, VLMCs are used to model the cluster assignment results on the weekly wildfire count time series data over a range of years. We provide preliminary information on clustering weekly wildfire counts using a model-based approach built from TBATS models that can help identify broad characteristics for an upcoming fire season through extensions of Markov chains.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/92174
note: Clustering Wildfire Occurrence TimeSeriesbySimon Marcus SnymanB.Sc. Mathematics, The University of British Columbia, 2023A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Mathematics)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)August 2025© Simon Marcus Snyman, 2025The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Clustering Wildfire Occurrence Time Seriessubmitted by Simon Marcus Snyman in partial fulfilment of the require-ments of the degree of Master of ScienceDr. W. John Braun, Irving K. Barber Faculty of ScienceCo-supervisorDr. Lengyi Han, Irving K. Barber Faculty of ScienceCo-supervisorDr. Irene Vrbik, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Michael Noonan, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Frederic Menard, Irving K. Barber Faculty of ScienceUniversity ExamineriiAbstractExponential smoothing methods offer several tools for forecasting andsimulating varying time series patterns, including historical wildfire counts.The Box-Cox transform, ARMA errors, trend and seasonal components(BATS), and the trigonometric BATS model (TBATS) are exponential smooth-ing techniques capable of modeling complex seasonality, non-integer frequen-cies, and more. The BATS and TBATS frameworks support short- andintermediate-term forecasting and simulation, while also providing a foun-dation for model-based time series clustering of wildfire counts.The application in this thesis of the BATS and TBATS models aimsto support fire agencies in forecasting and simulating wildfire occurrencesacross Canada on an increased scale that provides alternative methods forfuture use.Variable length Markov chains (VLMCs) are an extension of traditionalfixed-order Markov chains that can reduce model complexity and improvecomputational efficiency. To assess the practical…
has type: Thesis/Dissertation
date issued: 2025-09
is shown at: 10.14288/1.0449992
language: eng
degree discipline: Mathematics
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: Braun, John, 1963-
contributor: Han, Lengyi
type: Text
identifier URI: http://hdl.handle.net/2429/92174