Reza Miry, a Master of Science candidate in the Department of Mathematics and Statistics, will present their Masters thesis titled Time Series Prediction: HMMs with TAN and Bayesian Network Observation Structures on Monday, September 23, 2024 at 9:30 AM online on Microsoft Teams.

The examination committee includes Stephen Anco, Chair; Pouria Ramazi and Tianyu Guan, Co-Supervisors; Rahul G. Krishnan, External Examiner (University of Toronto); and William Marshall and Xiaojian Xu, Committee Members.

Students (both graduate and undergraduate) as well as other members of the Brock Community are invited to attend. If you are interested in the presentation, please contact Neil Marshall at nmarshall@brocku.ca for a link to the Team.

Abstract: This thesis addresses key challenges in time series classification, focusing on enhancing predictive accuracy through innovative modeling techniques. First, we introduce TAN-HMM, an extension of the traditional Hidden Markov Model (HMM) that incorporates Tree-Augmented Naive Bayes (TAN) to account for correlated features, significantly improving classification performance on complex datasets like MSRC-12. Next, we propose the Bayesian Network Hidden Markov Model (BN-HMM), which combines the temporal dynamics of HMMs with the structural flexibility of Bayesian Networks, achieving superior accuracy and feature relationship discovery. Finally, we tackle the problem of robust early warning signals for disease outbreaks, utilizing cutting-edge deep learning models to predict emerging disease behavior from simulated and real-world noisy datasets. Together, these contributions push the boundaries of time series classification and offer practical solutions for real-world applications, from human activity recognition to disease outbreak prediction.