Complete Rare Event Specification using Stochastic Treatment: CRESST

In the fast moving world today, rare events are becoming increasingly common. Ranging from studying incidents of safety hazards to identifying transaction fraud, they all fall under the radar of rare events. Identifying and studying rare events become of crucial importance, particularly when the underlying event conforms to a sensitive or an adverse issue. The thing to note here is, despite the probability of occurrence being very close to zero, the potential specification of the rare event could be quite extensive. For example, within the parent rare event of Product Safety, there could be multiple types of potential hazard, rendering the sub-classes rarer still. In this paper, we are going to explore a novel algorithm designed to study a rare event and its sub-classes over time with primary focus on forecast and detecting anomalies. The anomalies studied here are relative anomalies i.e., they may not contribute to the long-term trend of the rare time series but represent deviation from the base state as seen in the immediate past.