Computing an explainable event horizon estimate

Abstract

A computer-implemented method of predicting an event horizon is disclosed. The method comprises maintaining condition data indicating a plurality of conditions occurring on one or more physical systems at a plurality of points in time. The method further comprises receiving an input feature vector representing a given condition occurring at a specific time during a specific period of time. The method also comprises generating, using a particular trained machine learning model of a plurality of trained machine learning models, a forecast value that indicates an amount of time from the specific time to an occurrence of a particular target condition on a particular physical system, the particular target condition being different from the given condition, each trained machine learning model corresponding to a distinct target condition. In addition, the method comprises causing, based on the forecast value, an action to be executed on the particular physical system.