How to implement Hidden Markov Model on multiple columns?

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I'm having trouble implementing a HMM model. I'm starting with a pandas dataframe where I want to use two columns to predict the hidden state. I'm using the hmmlearn package.

I'm following the instructions for hmmlearn 'Working with multiple sequences" https://hmmlearn.readthedocs.io/en/latest/tutorial.html#multiple-sequences

I followed the code below but set X1 and X2 as my columns

X1 = [[0.5], [1.0], [-1.0], [0.42], [0.24]]
X2 = [[2.4], [4.2], [0.5], [-0.24], [0.24]]
X = np.concatenate([X1, X2])
lengths = [len(X1), len(X2)]
hmm.GaussianHMM(n_components=3).fit(X, lengths)
predictions=model.predict(X)

The problem is that when I try to predict the state instead of combining the sequences to create 1 prediction, i'm getting a prediction for each observations. So in this example I want 5 observations but I'm getting 10. Is there a way to incorporate the features of a dataframe as independent variables to get 1 combined prediction?

markov hmmlearn pandas hidden python
1 Answers

Currently you are giving the model two sequences of samples, each sequence having 5 observations with only one feature - so 10 observations in total. What you want is to have a single sequence with 5 observations, with two features.

Would be something like that:

X = [[0.5, 2.4], [1.0, 4.2], [-1.0, 0.5], [0.42, -0.24], [0.24, 0.25]]
lengths = [len(X)]
model = hmm.GaussianHMM(n_components=3).fit(X, lengths)
predictions = model.predict(X)

Then you will have only five predictions.

If you want to build the X variable using X1 and X2like in your example, you can do it using zip:

X = [[x1[0], x2[0]] for x1, x2 in zip(X1, X2)]
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