Optimized re-training for analytic models

A computer program product for retraining an analytic model, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• building, by a processor, a Markov chain for the analytic model, wherein the Markov chain has only two states that consist of an alarm state and a no alarm state;
• updating, by the processor, the Markov chain with observed states, for each of a plurality of timestamps evaluated during a burn-in period;
• updating, by the processor, state transition probabilities within the Markov chain, for each of a plurality of timestamps evaluated after the burn-in period;
• generating, by the processor, a signal for causing the model to be retrained, responsive to any of the state transition probabilities representing a probability in a range of greater than 0.5 up to 1.0 of seeing the alarm state in a previous interval and again in a current interval;
• and
• retraining, by the processor or another processor, the model responsive to the signal for causing the model to be retrained.

A system for retraining an analytic model, comprising:

• a processor and a memory, configured to:
• build a Markov chain for the analytic model, wherein the Markov chain has only two states that consist of an alarm state and a no alarm state;
• update the Markov chain with observed states, for each of a plurality of timestamps evaluated during a burn-in period;
• update state transition probabilities within the Markov chain, for each of a plurality of timestamps evaluated after the burn-in period;
• generate a signal for causing the model to be retrained, responsive to any of the state transition probabilities representing a probability in a range of greater than 0.5 up to 1.0 of seeing the alarm state in a previous interval and again in a current interval;
• and
• retraining the model responsive to the signal for causing the model to be retrained.