An Uncertainty-Aware ED-LSTM for Probabilistic Suffix Prediction

• URL: http://arxiv.org/abs/2505.21339v2
• Date: Fri, 06 Jun 2025 09:03:44 GMT
• Title: An Uncertainty-Aware ED-LSTM for Probabilistic Suffix Prediction
• Authors: Henryk Mustroph, Michel Kunkler, Stefanie Rinderle-Ma,
• Abstract summary: Suffix prediction of business processes forecasts the remaining sequence of events until process completion.<n>We propose probabilistic suffix prediction, a novel approach that approximates a probability distribution of suffixes.<n>The proposed approach is based on an Uncertainty-Aware-Decoder LSTM (U-ED-LSTM) and a Monte Carlo (MC) suffix sampling algorithm.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Suffix prediction of business processes forecasts the remaining sequence of events until process completion. Current approaches focus on predicting the most likely suffix, representing a single scenario. However, when the future course of a process is subject to uncertainty and high variability, the expressiveness of such a single scenario can be limited, since other possible scenarios, which together may have a higher overall probability, are overlooked. To address this limitation, we propose probabilistic suffix prediction, a novel approach that approximates a probability distribution of suffixes. The proposed approach is based on an Uncertainty-Aware Encoder-Decoder LSTM (U-ED-LSTM) and a Monte Carlo (MC) suffix sampling algorithm. We capture epistemic uncertainties via MC dropout and aleatoric uncertainties as learned loss attenuation. This technical report presents a comprehensive evaluation of the probabilistic suffix prediction approach's predictive performance and calibration under three different hyperparameter settings, using four real-life and one artificial event log. The results show that: i) probabilistic suffix prediction can outperform most likely suffix prediction, the U-ED-LSTM has reasonable predictive performance, and ii) the model's predictions are well calibrated.

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