I am using Hidden Markov Models, having observations as continuous variable and states as discrete variable. I can use the observations to train HMM model and generate n number of states(say 2 hidden states). However, once I am done with the modelling, I know I would be having 2 hidden states and associated parameters of HMM(like initial state probabilities).
However, do we some mechanism to infer the true meaning of those states. Because now I have 2 states S1 and S2, but I don't know what those states represent.
I want to create the states and give true meaning to them. To explain by example, lets assume, I have time series data for Sales and I would like to create 2 hidden states(say Winter/Summer). How do I model this type of hidden states.