Finite mixture of hidden Markov models for tensor-variate time series data

Abdullah Asilkalkan (), Xuwen Zhu () and Shuchismita Sarkar ()
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Abdullah Asilkalkan: The University of Alabama
Xuwen Zhu: The University of Alabama
Shuchismita Sarkar: Bowling Green State University

Advances in Data Analysis and Classification, 2024, vol. 18, issue 3, No 2, 545-562

Abstract: 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. In this study, a finite mixture of hidden Markov model 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.

Keywords: Finite mixture model; Hidden Markov model; Forward-backward algorithm; Tensor-variate time series; 62H30 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11634-023-00540-y

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