The paper investigates multi‑horizon forecasting of event sequences as a conditional generative modeling problem, evaluating forecasts for both prediction accuracy and distributional realism. It introduces a benchmark with metrics, baselines, and real‑world datasets, revealing a trade‑off between accuracy and realism. The authors propose Wasserstein BaryBooster, an algorithm that combines samples from a realism‑strong diffusion model with a state‑of‑the‑art predictor to convert distributional realism into improved prediction accuracy.
By Dmitry Osin, Egor Surkov, Petr Mokrov, Igor Udovichenko, Ivan Karpukhin, Evgeny Burnaev, Egor Shvetsov, Alexander Korotin
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: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
arXiv:2501. 14291v3 Announce Type: replace Abstract: Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time.
By Feng Zhou, Quyu Kong, Jie Qiao, Cheng Wan, Yixuan Zhang, Ruichu Cai
The paper introduces a Unified Particle Filter LSTM (Unified PF‑LSTM) for data‑driven process simulation, which maintains a weighted set of recurrent‑state hypotheses to better capture latent process conditions from incomplete event logs. By summarizing this particle belief with a weighted mean and moment‑generating‑function features, the model predicts next‑activity probabilities and conditional sojourn‑time quantiles. Experiments on three real‑world emergency department datasets show that the framework consistently outperforms existing data‑driven baselines in reproducing routing, duration, and system‑level behavior, especially when process dynamics are only partially reflected in the logs.
By Parvin Malekzadeh, Opher Baron, Dmitry Krass
arXiv:2606. 01999v1 Announce Type: cross Abstract: Modern deep learning models for forecasting groups of time series rely on increasingly longer observation windows.
By Luca Butera, Giovanni De Felice, Andrea Cini, Cesare Alippi
arXiv:2602. 03564v2 Announce Type: replace Abstract: Time series forecasting can be viewed as a generative problem that requires both semantic understanding over contextual conditions and stochastic modeling of continuous temporal dynamics.
By Mingyue Cheng, Yaguo Liu, Daoyu Wang, Xiaoyu Tao, Qi Liu
arXiv:2607. 28035v1 Announce Type: new Abstract: Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing.
By Tianen Shen, Zhengyu Li, Yutong Li, Xiangfei Qiu, Xingjian Wu, Bin Yang, Jilin Hu
arXiv:2608. 06223v1 Announce Type: new Abstract: While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited.
By Yixiong Xiao, Congxi Xiao, Jingbo Zhou
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
arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.
By Zihao Yao, Qi Zheng, Jiankai Zuo, Yaying Zhang
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