I am reading this paper(Stock market forecasting using hidden Markov model: a new approach) and get confused about how they predict the next day's close price. Below is what the authors say about how to implement the hmm:
Using the trained HMM, likelihood value for current day’s dataset is calculated. For example, say the likelihood value for the day is ‘ , then from the past dataset using the HMM we locate those instances that would produce the same ‘ or nearest to the ‘ likelihood value. That is we locate the past day(s) where the stock behaviour is similar to that of the current day. Assuming that the next day’s stock price should follow about the same past data pattern, from the located past day(s) we simply calculate the difference of that day’s closing price and next to that day’s closing price. Thus the next day’s stock closing price forecast is established by adding the above difference to the current day’s closing price.
I just begin learning the hmm and know that in a hidden markov model, we have hidden states and observation states. So in order to train a hmm model, one needs to specify what is the hidden states and what is the observation state.But in their paper, I did not quite get what they use as the hidden states and observation to train the model. Besides, they mention a likelihood value which I do not understand. So my questions are: in this paper
1.what are the hidden states and what are the observation states.
2.what is the likelihood value and how to calculate it.