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.
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...
arXiv:2609.13345v1 Announce Type: cross Abstract: Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across...
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...
arXiv:2607. 12391v1 Announce Type: new Abstract: We present a diffusion based model for asynchronous time series prediction, where the goal is to predict the next inter event time and event type.
arXiv:2607. 00196v1 Announce Type: new Abstract: Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally distributional rather than pointwise.
arXiv:2603. 15055v3 Announce Type: replace-cross Abstract: We present a theory-guided generalized Bayesian methodology for spatio-temporal raster data, which we use to train an ensemble of stochastic feed-forward neural networks with Gaussian-distributed weights.
The Marformer is a Transformer model designed to predict conditional marginal distributions of missing variables given observed data, enabling computation of Bayes risk and Value of Information. It learns a hidden-vector representation for each variable’s distribution and refines these through attention, similar to BERT’s masked language modeling but for probabilistic inference. Evaluations on synthetic Bayesian networks, discretized Gaussians, structured annotation data, and a real annotation dataset show that the Marformer matches or surpasses classical missing-data methods while being faster and not requiring explicit joint distribution modeling.
arXiv:2608. 20025v1 Announce Type: new Abstract: Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation.
arXiv:2508. 09191v2 Announce Type: replace-cross Abstract: Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance.
arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.
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.
arXiv:2606. 02117v1 Announce Type: cross Abstract: Probabilistic time series forecasting has attracted increasing attention in financial applications due to the need to quantify risk and uncertainty in future observations.