Hugging Face Trending Papers June 1, 2026

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting

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Probabilistic time series forecasting has attracted increasing attention in financial applications due to the need to quantify risk and uncertainty in future observations. We propose ProbRes, a post-hoc probabilistic calibration method that explicitly learns and incorporates volatility dynamics into probabilistic forecasting, enabling effective handling of heteroskedastic data.

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arXiv Machine Learning

Jun 2

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting

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.

By Tingting Wang, Yunyi Zhang, Benyou Wang

arXiv Machine Learning

Jun 3

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in 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.

By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang

llms multimodal benchmarks safety

arXiv Machine Learning

Sep 15

Beyond Point Forecasts: A Survey on Probabilistic Forecasting for Time Series and Spatiotemporal Data

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...

By Donia Besher, Rajdeep Pathak, Madhurima Panja, Tanujit Chakraborty

arXiv Machine Learning

Aug 4

Latent-Regime Bias Auditing for Volatility Forecasting

arXiv:2608. 01599v1 Announce Type: new Abstract: Volatility forecasts are commonly evaluated with aggregate accuracy metrics such as RMSE and MAE, but these metrics can hide conditional failures that matter for risk management.

By Arthur Chagas, Pedro Bento, Yan Aquino, Arthur Buzelin, Wagner Meira Jr., Cristiano Arbex Valle

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arXiv AI

Aug 12

Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting

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

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arXiv Machine Learning

Aug 21

CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting

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.

By Alexander Marusov, Dmitry Anikin, Petr Sokerin, Vitaliy Pozdnyakov, Ilya Kuleshov, Alexey Zaytsev

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Related stories

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

Beyond Point Forecasts: A Survey on Probabilistic Forecasting for Time Series and Spatiotemporal Data

Latent-Regime Bias Auditing for Volatility Forecasting

Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting

CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting