Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes

TL;DR

This paper introduces methods for neural networks to estimate uncertainty in process time predictions, improving accuracy, confidence, and practical usability in business process monitoring.

Contribution

It applies Bayesian neural network techniques to predictive process monitoring, enabling uncertainty estimation and confidence intervals for the first time in this domain.

Findings

Uncertainty estimates improve prediction quality.

Methods are fast and applicable to small datasets.

Uncertainty enables better decision-making and human collaboration.

Abstract

Artificial neural networks will always make a prediction, even when completely uncertain and regardless of the consequences. This obliviousness of uncertainty is a major obstacle towards their adoption in practice. Techniques exist, however, to estimate the two major types of uncertainty: model uncertainty and observation noise in the data. Bayesian neural networks are theoretically well-founded models that can learn the model uncertainty of their predictions. Minor modifications to these models and their loss functions allow learning the observation noise for individual samples as well. This paper is the first to apply these techniques to predictive process monitoring. We found that they contribute towards more accurate predictions and work quickly. However, their main benefit resides with the uncertainty estimates themselves that allow the separation of higher-quality from lower-quality predictions and the building of confidence intervals. This leads to many interesting applications, enables an earlier adoption of prediction systems with smaller datasets and fosters a better cooperation with humans.