Downside-Controlled Online Forecast Combination under Delayed and Revised Outcomes

arXiv:2609.29096 · cs.LG · Submitted 2026-09-24 · Read on arXiv

cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Benchmark — 53× in this paper · explained in 2 episodes
• Calibration — 24× in this paper · explained in 7 episodes
• Adaptation — 18× in this paper · explained in 1 episode
• Foundation Model — 17× in this paper · explained in 3 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Aggregation — 16× in this paper · explained in 1 episode
• Foundation Models — 13× in this paper · explained in 7 episodes
• Regret — 13× in this paper · explained in 3 episodes
• Time Series Forecasting — 10× in this paper · explained in 3 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Transformer — 6× in this paper · explained in 2 episodes
• Distribution Shift — 4× in this paper · explained in 6 episodes
• In International Conference on Learning Representations — 3× in this paper
• Non-stationarity — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adaptive Intervals — 2× in this paper · explained in 1 episode
• Concept Drift — 2× in this paper · explained in 4 episodes
• Frozen model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• In International Conference on Machine Learning — 2× in this paper
• Information Leakage — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Conformal Prediction — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes

Sources

• Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models
• Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data
• Minusformer: Improving Time Series Forecasting by Progressively Learning Residuals

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