My Data is:
- TimeStamp <- time stamp of the event occurring
- Length <- length is the duration of the event
- ID <- identifier where the event is occurring ( 25 IDs)
TimeStamp | Length | ID
The state of the machine between the events is 'upstate' and during the event is 'downstate'.
Q 1) I want to predict the probability of next event happening and the approximate length for it.
Q 2) At a given time 't' in the future, I want to predict the if the state of the machine is 'upstate' or 'downstate'
Of what I read, Hidden Markov models would be a good approach to use on this dataset. Are there any other suggestions that would be help me?
I am new to this type of datasets.