The paper introduces two methods for modelling aggregated supply and demand curves in the EPEX SPOT Day‑Ahead market. The first is a low‑dimensional parametric approach that produces deterministic point forecasts using plateau levels, elastic‑region boundaries, polynomial coefficients, and XGBoost. The second is a high‑dimensional generative approach based on conditional Denoising Diffusion Probabilistic Models that samples plausible curves from price arrivals and volume‑increment marks, enabling analysis of price and volume sensitivity and price impact.
By Julian Gutierrez, Redouane Silvente
arXiv:2601. 20226v2 Announce Type: replace Abstract: We propose two methodologies for modelling aggregated supply and demand curves in the EPEX SPOT Day\char45 Ahead market, emphasizing generative models as a way to recover distributional variability.
By Julian Gutierrez, Redouane Silvente
The paper introduces Physics‑SIMS‑TS, a conditional diffusion model designed for long‑horizon oil and gas production forecasting. It enforces monotone decline through negative guidance, decline‑curve constraints, and isotonic projection during sampling, and incorporates spatial training augmentation and an ensembled stochastic sampler to produce calibrated predictive distributions. Evaluated on over 35,000 wells across three jurisdictions, Physics‑SIMS‑TS achieves the highest accuracy among diffusion forecasters and matches transformer ensembles, with only a 0.5% increase in mean squared error for monotonicity.
By Temesgen Mikael Abraha, Yves Lucet
DiTS introduces a Multimodal Diffusion Transformer designed for time‑series forecasting that incorporates heterogeneous exogenous covariates. The model treats endogenous targets and exogenous covariates as separate modalities, jointly conditioning future generation on target history and available covariates. By employing flow matching and Time‑aligned Modulation, DiTS achieves strong deterministic and probabilistic forecasting performance across diverse covariate‑aware tasks.
By Haoran Zhang, Haixuan Liu, Yong Liu, Yunzhong Qiu, Yuxuan Wang, Jianmin Wang, Mingsheng Long
The paper introduces a new diffusion-based approach for probabilistic weather forecasting that incorporates auxiliary conditional denoising tasks. By predicting future states from both clean and corrupted contexts, the model reduces ambiguity and learns conditional distributions across noise levels using a proper scoring rule. Experiments demonstrate improved one‑step forecasting, especially at longer horizons, and better calibration in high‑dimensional global weather scenarios.
By Joonhyeong Park, Giung Nam, Hyungi Lee, Kyunghyun Cho, Byoungwoo Park, Juho Lee
KiT is a K‑line Diffusion Transformer foundation model designed for financial time‑series forecasting. It reframes future prediction as conditional path generation via flow matching, producing ensembles of plausible OHLCV trajectories from a historical context window. Trained on billions of candlestick bars across multiple markets and timescales, KiT achieves superior RankIC scores compared to task‑specific forecasters and general time‑series models.
arXiv:2607. 27188v1 Announce Type: new Abstract: Accurate option prices do not imply accurate recovery of the latent risk-neutral density.
By Lennon J. Shikhman, Michael Galarnyk, Aadi Dash, Nicholas A. Welsh
Accurate option prices do not imply accurate recovery of the latent risk-neutral density. We study this distinction with two complementary benchmarks.
The paper introduces Distribution‑Conditioned Transport (DCT), a framework that learns transport maps conditioned on embeddings of source and target distributions, allowing generalization to unseen distribution pairs. DCT supports semi‑supervised learning for distributional forecasting by leveraging distributions observed at only one condition. It is agnostic to the transport mechanism and is demonstrated on synthetic benchmarks and four biological applications, including batch effect transfer in single‑cell genomics and modeling T‑cell receptor sequence evolution.
By Nic Fishman, Gokul Gowri, Paolo L. B. Fischer, Marinka Zitnik, Omar Abudayyeh, Jonathan Gootenberg
The paper presents a comparative study of six deep learning models—state-space, MLP, RNN, and Transformer-based architectures—for cross-border electricity price forecasting using publicly available data. It focuses on generalization across markets and evaluates performance under low-data target-market conditions (zero-shot, one-shot, few-shot) with a standardized dataset for the Germany‑Luxembourg bidding zone in 2024. Results show that N‑HiTS and NBEATSx perform competitively in limited‑data scenarios, while transformer models achieve comparable accuracy but require more adaptation and tuning, and that careful feature selection and hyperparameter tuning improve performance.
By Hadeer Elashhab, Sai Srijan Papineni, Marvin Dorn, Veit Hagenmeyer, Benjamin Sch\"afer
arXiv:2608. 11114v1 Announce Type: cross Abstract: Probabilistic forecasting plays an essential role in risk-sensitive decision-making, particularly in long-horizon settings.
By Kiran Madhusudhanan, Christian Kl\"otergens, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi
The paper introduces DV-Flow, a flow‑matching framework that treats deterministic prediction as a parallel velocity component instead of embedding it within the generative process. This design stabilizes end‑to‑end training and unifies several existing methods as special cases, enabling direct comparison. Experiments on six real‑world datasets show that DV‑Flow consistently outperforms strong baselines in probabilistic time‑series forecasting.