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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.

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  • $\begingroup$ Great question. I edited your title to make the title more specific. Please check whether you are content with the new title. $\endgroup$ – Ferdi Sep 4 '18 at 15:53
  • $\begingroup$ @Ferdi: great no worries! I just hope to get an answer for this though. $\endgroup$ – Darpit Sep 4 '18 at 22:06

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