ML methods for prediction, using categorical variables and time
One way to frame the problem of predicting patterns of complex discrete states is probabilistic graphical models (PGMs). PGMs model joint (multivariate) distributions over large numbers of random variables that interact with each other.
PGM do not have a notion of linear time. They model state. Given the current state and previous states, how likely are other states in the next time step? In your problems, state is the type and location of alert.
Common examples of PGMs are Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs).