I want to make a thing that produces a stochastic process from time series data. The time series data is recorded every hour over the year, which means 24-hour of patterns exist for 365 days.
What I want to do is something like below:
Fit a probability distribution using data for each hour so that I can sample the most probable value for this hour.
Repeat it for 24 hours to generate a pattern of a day.
BUT! I want the sampling to be done considering previous values rather than being done in an independent manner.
For example, I want to sample from or just rather than when refers to a specific hour.
What I came up with was the Markov chain, but I couldn't find any reference or materials on how to model it from real data.
Could anyone give me a comment for this issue?