Recently, I was asked to provide the functionality to detect temporal anomalies outside of ProM. The idea is to use the information encoded in the stochastic Petri net models to determine outlier regions. A simple test is then enough to see whether a new observed duration is anomalous (e.g. falls into the category of the 5 % most extreme cases).
This short screencast shows how we can extract the anomaly intervals from a stochastic Petri net model. We can then use the output JSON file in conformance checking applications without bothering with the duration distributions of the activities in the process.