- Define the number of hidden states, the order of the AR process, and initialize the transition matrix, emission parameters (AR coefficients), and state probabilities.
- Use the Expectation-Maximization algorithm to iteratively estimate the parameters of the ARHMM.
- Use the Forward-Backward algorithm to compute the probabilities of the hidden states given the observed data.
- Update the AR coefficients and transition probabilities based on the results of the EM algorithm.
Autoregressive HMM implementation?
5 views (last 30 days)
Show older comments
Hello MATLAB community,
Is there any Autoregressive hidden Markov model (ARHMM) implementations available in MATLAB? I know that there are AR model functions but I cannot find any for the HMM.
Ashley
0 Comments
Answers (1)
Aman
on 9 Oct 2024
I didn't find any out-of-box implementation available for ARHMM in MATLAB, but in order to implement it in MATLAB, you can follow the below steps:
I hope this will help you to proceed ahead with your workflow :)
0 Comments
See Also
Categories
Find more on Conditional Mean Models in Help Center and File Exchange
Products
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!