[2604.00653] Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction

Computer Science > Machine Learning arXiv:2604.00653 (cs) [Submitted on 1 Apr 2026] Title:Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction Authors:Marwan Hassani, Tamara Verbeek, Sjoerd van Straten View a PDF of the paper titled Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction, by Marwan Hassani and 2 other authors View PDF HTML (experimental) Abstract:Predictive process monitoring (PPM) focuses on predicting future process trajectories, including next activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume a static environment, overlooking dynamic characteristics and concept drifts. This results in catastrophic forgetting, where training while focusing merely on new data distribution negatively impacts the performance on previously learned data distributions. Continual learning addresses, among others, the challenges related to mitigating catastrophic forgetting. This paper proposes a novel approach called Continual Next Activity Prediction with Prompts (CNAPwP), which adapts the DualPrompt algorithm for next activity prediction to improve accuracy and adaptability while mitigating catastrophic forgetting. We introduce new datasets with recurring concept drifts, alongside a task-specific forgetting metric that measures the prediction accuracy gap between initial occurrence and subsequent task o...