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I have a rough conceptual understanding of what Hidden Markov Models do.

What I don't understand is how to really create/train one. Let me outline what I'm working on, and then I'll give more specific questions.

(In case it is important, I am using the depmixS4 package in R.)

I am working on a time series dataset, where my fundamental task is to predictively (not retrospectively) identify zones of high and low volatility/activity, so I decided to try Hidden Markov Models to see if I could have it identify whether at time point x (not given any future data), we are in some "high/med/low volatility states".

1. How many features/variables is reasonable to give to the model?

Most blogs/posts/articles I've seen on the topic only feed a single variable into the model before telling it how many states to identify. I've seen a couple put in 2 variables, but no more. Is there an issue with putting in 5 or 10 different variables?

2. How to tell if a variable is helpful or not?

Is there any way I can tell if adding a variable was useful or not, besides visual inspection of the output states and subjectively judging whether that variable made a helpful difference?

3. What kinds of features/variables to use for the HMM?

I realize this is an extremely broad and subject-specific question, but essentially: should I design and choose features for the HMM just like I would for any machine learning algorithm like a RandomForest? I've only seen EXTREMELY basic features be fed into HMMs, is there a reason for that? Could I use features more complex than, for example, "percent change over last time step"?

Thank you for your time and help!

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