Multivariate Probabilistic Time Series Forecasting with Informer

Probabilistic Time Series Forecasting with 🤗 Transformers

ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting

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.

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.

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.

Global Explanations for Multivariate Time Series Forecasting Models via $K$-Order Markov Approximations

arXiv:2606. 27599v1 Announce Type: cross Abstract: While many explainable AI (XAI) methods have been proposed, most are not designed for time-series forecasting models and often rely on the implicit assumption that timestamp features are independent.

Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting

arXiv:2606. 10592v1 Announce Type: new Abstract: Time series forecasting often suffers from over-smoothing, especially when future dynamics are multi-modal.

SPACE: Sample-cloud Predictive Adaptive Conformal Ellipsoids for Multivariate Time-Series Forecasting

arXiv:2608. 17333v1 Announce Type: cross Abstract: Modern probabilistic time-series forecasters often express uncertainty through forecast samples.

Information Theory and Ensemble Models

How should we ensemble time-series forecasts better? The post Information Theory and Ensemble Models appeared first on Towards Data Science .

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting

arXiv:2607. 22299v1 Announce Type: cross Abstract: Forecasting multiple time-series with high-dimensional covariates presents a core challenge: unifying common temporal patterns while retaining meaningful series-specific information.

AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret

arXiv:2606. 02663v1 Announce Type: cross Abstract: Recent advances in machine learning have produced probabilistic weather forecasting models comparable to state-of-the-art numerical weather predictors.

Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence

arXiv:2607. 02623v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary settings is underexplored.

Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting

arXiv:2606. 19560v1 Announce Type: new Abstract: Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-health need.