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.