ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting

ArXi:2606.02117v1 Announce Type: cross 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. During