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1. Hidden markov model matlab code forecasting series#
2. Hidden markov model matlab code forecasting windows#

I might have worded this a little awkwardly so I hope it is clear what I mean. During training you would need to force the HMM to only assign positive probability to transitioning to a state at time $t$ where $k$ matches the label at time $t$. If you have $K$ classes, allocate $N$ states for each class (so $N \times K$ states altogether) of the form $s_$, $k = 1, \ldots, K$, $i=1,\ldots N$. You could also use one big HMM to achieve something similar. For example, you could let the states in the top-level represent the classes and then allow the lower level HMMs to model the temporal variation within classes.

If you are convinced this is a bad idea, or find it performs poorly but still want to stick with generative models, you could use something like a Hierarchical HMM. Furthermore you can just fit the emission probabilities for each state based on you're observed data and corresponding class, ignoring the temporal aspects. Estimating the transition probabilities is easy, you just count essentially. The estimation problem is also significantly easier in this case. Have you tried this? It may not work as poorly as you think. You mentioned over at reddit that you were hesitant to assign a single state for each class. This doesn't seem to be the case for you problem since you effectively have one large length $T$ time series. Notice how this rests on the assumption I can break sequences up into the meaningful chunks to be classified before I compare posteriors.

For example, if I was classifying the sentiment of sentences as positive or negative, I could build an HMM for each as you've described. The approach you describe for using HMMs for classification is really only applicable to settings where you have independent sequences you want to classify. It is a brain-computer interface problem using the Berlin BCI Competition data.

Hidden markov model matlab code forecasting windows#

The aim is to be able to classify EEG data windows to a given mental state having trained upon the labelled data. How do I then train the HMM on this data? If it helps I am trying to use the pmtk3 toolkit, but I open to using anything really - it just has to be able to deal with real-valued observations as the power spectral densities are continuous not discrete (the default MATLAB toolbox can only deal with discrete observations). Where t is less than T and denotes the size of each window.

This can be divided into windows which I know from the experimental protocol (the data is labelled) and so I can gather together sets of 96*t matrices for each class. To be more concrete I have some EEG data which is a 96xT matrix where I have 96 feature vectors which are the power spectral densities of different frequencies from different channels and T is the length of time of the signal (at some sampling rate)

Hidden markov model matlab code forecasting series#