# Should I use Markov or Hidden Markov model when there are only 2 possible states?

I am just learning Markov Models and HMM. I have a problem on which I am trying to implement them but am not sure which one is appropriate. Following is the problem.

The data is time-series. There are 2 states - 1 & 0. When at state 1, observations can take a range of values usually between say 1 to 10000. But when in state 0 the observation always takes the value of 0. There are other independent variables which are correlated to the observations i.e. they can be used to predict the observation values but they are not available in the data.

I have data till today. I don't what the state or observations will be for the next week. I also have to predict the probability of the occurrence of the states/observation from the previous step.

I read the paper on Markov by Rabiner. While since the states in my case are not Hidden I initially thought it is direct Markov Chain Model but in the paper Rabiner uses the Coin-toss as an example for HMM even though the states in a coin-toss are not really hidden.