- 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?
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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
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Aman
le 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 :)
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