Time Series Foundation Models for Process Model Forecasting

Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by mode ling the temporal dynamics of directly-follows (DF) relations, comple menting predictive process monitoring that focuses on single-case prefixe s. Prior benchmarks show that machine learning and deep learning models pr ovide only modest gains over statistical baselines, mainly due to the s parsity and heterogeneity of the DF time series. We investigate Time Ser ies Foundation Models (TSFMs), large pre-trained models for generic t ime series, as an alternative for PMF. Using DF time series derived from rea l-life event logs, we compare zero-shot use of TSFMs, without additional training, with fine-tuned variants adapted on PMF-specific data. TSFMs generally achieve lower forecasting errors (MAE and RMSE) than tradit ional and specialized models trained from scratch on the same logs, in dicating effective transfer of temporal structure from non-process do mains. While fine-tuning can further improve accuracy, the gains are ofte n small and may disappear on smaller or more complex datasets, so zero-s hot use remains a strong default. Our study highlights the generaliza tion capability and data efficiency of TSFMs for process-related time series a nd, to the best of our knowledge, provides the first systematic evaluat ion of temporal foundation models for PMF.