Most of the time series analysis tutorials/textbooks I found time series data, usually deal with continuous numerical variables. I am currently trying to solve a problem that deals with multivariate time series data, where the fields are all categorical variables.
Specifically, my data is a stream of alert data, where at each time stamp, information such as the alert monitoring system, the type of alert, the location of the problem, etc. are stored in the alert. These fields are all categorical variables.
My data is very similar to the one described in another question, so I used a similar description. However my question is a bit more general.
Given a chain of alerts, what is a good approach for using ML to predict the location and type of the next alert (at t(1) ), having knowledge of common alert patterns and the values for t(0), t(-1), t(-2), ...t(-n) ?