PREDICTION OF NETWORK EVENTS VIA RULE SET REPRESENTATIONS OF MACHINE LEARNING MODELS

A processing system including at least one processor may obtain a time series of measurement values from a communication network and train a prediction model in accordance with the time series of measurement values to predict future instances of an event of interest, where the time series of measurement values is labeled with one or more indicators of instances of the event of interest. The processing system may then generate a deterministic finite automaton based upon the prediction model, convert the deterministic finite automaton into a rule set, and deploy the rule set to at least one network component of the communication network.