DES-JEPA introduces a joint-embedding predictive architecture that learns a shared discrete vocabulary from synthetic stochastic processes with unaligned event vocabularies. Coupled with DES-PFN, a prior‑fitted network that reuses this vocabulary for in‑context next‑event prediction, the framework can handle up to 700 distinct event types without task‑specific fine‑tuning. It achieves competitive zero‑shot performance on eight unseen real‑world datasets, addressing the limitations of current foundation models for high‑dimensional discrete event sequences.
By Hugo Math, Maximilian Forstenh\"ausler, Christos Anagnostopoulos
The paper introduces the Foundation Event Predictor (FEP), a scalable in‑context architecture that performs next‑event prediction across diverse event logs using learned, event‑level nonparametric inference. FEP employs causal self‑attention to encode context and target histories, cross‑attention for relevance‑weighted aggregation, and permutation‑robust embedding learning to decouple event IDs from semantics. Trained on 696 real‑world datasets and evaluated on 50 unseen ones, FEP generalizes effectively to new systems without parameter adaptation, often matching or surpassing models trained separately on each target dataset.
arXiv:2610.17503v1 Announce Type: cross
Abstract: Event logs are ubiquitous across real-world domains, including business processes, manufacturing workflows, healthcare, and many other operational sy...
By Fabian Spaeh, Sean Jaffe, Shandian Zhe, Bin Shen
arXiv:2607. 27797v1 Announce Type: new Abstract: Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event.
By Lennart Fertig, Lukas Kirchdorfer, Tobias Sesterhenn
arXiv:2608. 10120v1 Announce Type: new Abstract: Modern sequence models, from Transformers to State Space Models, have enabled powerful generative modeling across diverse domains, yet they are typically trained to predict what happens while treating when it happens as a secondary concern.
By Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji, Francesco Bronzino
arXiv:2606. 15868v1 Announce Type: new Abstract: Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering.
By Hans Weytjens, Ingo Weber
arXiv:2609.21382v1 Announce Type: new
Abstract: Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability...
By Jiaxin Yuan, Daniela Grigori, Han van der Aa
arXiv:2512.07624v2 Announce Type: replace
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...
By Yongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt
The paper introduces ACTS-WM, a self‑supervised framework that models latent actions underlying time‑series transitions. It combines a Joint‑Embedding Predictive Architecture encoder with an action‑conditioned world model to predict future representations conditioned on inferred latent actions and task information. Experiments on 22 datasets across four tasks show that ACTS‑WM improves downstream performance compared to pretrained JEPA‑based encoders.
By Chia-Han Kuo, Chengshuai Zhao, Huan Liu, Lo Pang-Yun Ting
arXiv:2609.24579v1 Announce Type: new
Abstract: Event logs arise in a wide range of real-world processes, capturing not only event activities and timestamps but also multi-modal contextual informatio...
By Fabian Spaeh, Jingxing Fang, Shandian Zhe, Bin Shen
The paper introduces SiamSA-PPM, a self‑supervised framework that augments event logs for predictive process monitoring by generating realistic trace variants using control‑flow semantics and frequent behavioral patterns. It combines Siamese learning with statistically grounded data augmentation to learn generalizable prefix representations without requiring labeled data. Experiments on diverse real‑world logs show that SiamSA-PPM matches or outperforms state‑of‑the‑art baselines in next‑activity and final‑outcome prediction tasks.
By Sjoerd van Straten, Alessandro Padella, Marwan Hassani
D-TAIA is a framework that adapts large language models for multi‑task predictive process monitoring, jointly predicting the next activity and remaining time of ongoing cases. It uses domain‑aware triplet loss pre‑training, FAISS‑based nearest‑neighbor retrieval for time estimation, and a TAIA inference strategy to preserve sequential reasoning while fine‑tuning a 10 M‑parameter backbone. Across four real‑world event logs, D‑TAIA achieves state‑of‑the‑art or competitive results compared to a fine‑tuned LLM and a recurrent neural network baseline, with ablation studies showing the effectiveness of NLP and computer‑vision techniques for this domain.
By Sjoerd van Straten, Christine Jacob, Marwan Hassani