Daniela Grigori

Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes

The resulting model matches discriminative baselines on point accuracy, dominates them on the calibration and sharpness of remaining-time distributions, and is the cheapest at inference, since a full predictive distribution is obtained in a single forward pass without sampling.