arXiv Machine Learning
Rollcast: Proper-Score Gated Rolling Anchors for Adaptive Probabilistic Time-Series Forecasting
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arXiv Machine Learning
Aug 19
Dynamic Regime-Aware Conformal Calibration for Reliable Economic Forecast Intervals under Multiple Distribution Shifts
Dynamic Regime-Aware Conformal Prediction (DRACP) is a new method that blends density‑ratio estimation, localized kernel weighting, and probabilistic regime‑aware weighting with a self‑tuning online significance controller to produce reliable prediction intervals under multiple distribution shifts. The authors prove finite‑sample validity with oracle weights, provide a coverage‑gap bound for estimated weights, and give deterministic or regret guarantees for the online controller. In experiments on 48 real forecasting series—including euro‑area inflation, US macroeconomic and energy indicators, and daily financial data—DRACP achieves the most reliable calibration, maintaining coverage close to the nominal 0.90 and never falling below 0.80, while other methods achieve narrower intervals but with higher under‑coverage. whyItMatters":"DRACP offers a principled trade‑off between calibration and efficiency, ensuring that prediction intervals meet coverage standards even when economic data exhibit covariate shift, concept drift, and latent regimes."
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.
arXiv Machine Learning
Aug 31
Generalized Gibbs Ensemble Weighting for Forecast Combination
The paper introduces Generalized Gibbs Ensemble Weighting (GGEW), a probabilistic framework that assigns weights to forecasting models using a Gibbs-style exponential transformation of normalized predictive loss. GGEW extends basic weighting through numerical stabilization, diversity-aware score corrections, and online hyperparameter adaptation, yielding variants such as Stable Gibbs weighting, Directional Gibbs-NCL, and Symmetric Gibbs-NCL. The authors evaluate GGEW on M4 competition submissions and real-world datasets (Monash Traffic, Electricity, Solar), finding that Gibbs-style adaptive weighting is competitive across various settings, though performance varies by dataset, horizon, and deployment protocol.
arXiv Machine Learning
Jun 9
Report the Floor: A Training-Free Conformal Interval Is a Mandatory Baseline for Probabilistic Time-Series Forecasting
arXiv:2606. 09473v1 Announce Type: cross Abstract: Probabilistic forecasters are increasingly learned, yet the baselines they are compared against are often weak or omitted.
arXiv AI
Jun 4
Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty
arXiv:2606. 04342v1 Announce Type: cross Abstract: Multi-step time series forecasting (MSF) is commonly evaluated using point-wise error metrics such as mean squared error (MSE), implicitly treating the conditional mean as a sufficient target.
arXiv Machine Learning
2d ago
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...