Finite Mixture of Hidden Markov Models for Tensor-variate Time Series Data

Music City Center 

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.

Finite Mixture model

Hidden Markov model

Forward-backward algorithm

tensor-variate time series 

Section on Statistical Computing