SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models

arXiv:2609.28582 · cs.LG, cs.AI, stat.ML · Submitted 2026-09-23 · Read on arXiv

cs.LG, cs.AI, stat.ML

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/amazon-science/chronos-forecasting

Terminology

• Alignment — 15× in this paper · explained in 1 episode
• Uncertainty Quantification — 13× in this paper · explained in 8 episodes
• Foundation Model — 11× in this paper · explained in 3 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Foundation Models — 7× in this paper · explained in 7 episodes
• Excess Area Under the Risk-Coverage Curve — 4× in this paper
• Time Series Forecasting — 4× in this paper · explained in 3 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Horizon Uncertainty Quantification — 3× in this paper
• Semantic Density — 3× in this paper
• Semantic Entropy — 3× in this paper
• Shifting Attention to Relevance — 3× in this paper
• Calibration — 2× in this paper · explained in 7 episodes
• Mean Absolute Scaled Error — 2× in this paper
• Native Calibration — 2× in this paper
• Normalized Excess Area Under the Risk-Coverage Curve — 2× in this paper
• Perplexity — 2× in this paper · explained in 3 episodes
• Predictive Entropy — 2× in this paper
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Time Series Foundation Models (TSFMs) — 2× in this paper · explained in 2 episodes
• Aggregated Relative Mean Absolute Scaled Error — 1× in this paper
• Aleatoric Uncertainty — 1× in this paper · explained in 5 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Dynamic Time Warping — 1× in this paper
• Dynamic Time Warping (DTW) — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hallucination — 1× in this paper · explained in 4 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Uncertainty Decomposition — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Chronos-2: From Univariate to Universal Forecasting
• Chronos: Learning the Language of Time Series
• ProbFM: Probabilistic Time Series Foundation Model with Uncertainty Decomposition
• When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting
• Language Models (Mostly) Know What They Know
• Scaling Laws for Neural Language Models
• Sundial: A Family of Highly Capable Time Series Foundation Models
• Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
• Aurora: Towards Universal Generative Multimodal Time Series Forecasting

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