A0165
Title: Finite mixture of hidden Markov models for tensor-variate time series data
Authors: Abdullah Asilkalkan - The University of Alabama (United States)
Xuwen Zhu - The University of Alabama (United States) [presenting]
Shuchismita Sarkar - Bowling Green State University (United States)
Abstract: The need to model data with higher dimensions, such as a tensor-variate framework where each observation is considered a three-dimensional object, increases due to rapid improvements in computational power and data storage capabilities. A finite mixture of hidden Markov models for tensor-variate time series data is developed. Simulation studies demonstrate high classification accuracy for both cluster and regime IDs. To further validate the usefulness of the proposed model, it is applied to real-life data with promising results.
