Time Series Foundation Models for Process Model Forecasting
cs.LG, cs.AI
Submitted: 2025-12-08
Updated: 2026-09-28
Comments: Revised version with minor corrections
License: http://creativecommons.org/licenses/by/4.0/
The gist: Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of directly-follows (DF) relations, complementing
Terminology
Abstract
Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of directly-follows (DF) relations, complementing predictive process monitoring that focuses on single-case prefixes. Prior benchmarks show that machine learning and deep learning models provide only modest gains over statistical baselines, mainly due to the sparsity and heterogeneity of the DF time series. We investigate Time Series Foundation Models (TSFMs), large pre-trained models for generic time series, as an alternative for PMF. Using DF time series derived from real-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 traditional and specialized models trained from scratch on the same logs, indicating effective transfer of temporal structure from non-process domains. While fine-tuning can further improve accuracy, the gains are often small and may disappear on smaller or more complex datasets, so zero-shot use remains a strong default. Our study highlights the generalization capability and data efficiency of TSFMs for process-related time series and, to the best of our knowledge, provides the first systematic evaluation of temporal foundation models for PMF.
Sources
- Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?
- GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
- Chronos-2: From Univariate to Universal Forecasting
- Chronos: Learning the Language of Time Series
- LoRA Learns Less and Forgets Less
- On the Opportunities and Risks of Foundation Models
- In-Context Fine-Tuning for Time-Series Foundation Models
- Scaling-laws for Large Time-series Models
- TimeGPT-1
- Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models
- Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
- GPT-4o System Card
- Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
- Moirai 2.0: When Less Is More for Time Series Forecasting
- Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
- Domain Adaptation of LLMs for Process Data
- TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
- fev-bench: A Realistic Benchmark for Time Series Forecasting
- Gemini: A Family of Highly Capable Multimodal Models
- Parameter-Efficient Fine-Tuning for Foundation Models
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks