Dynamic Multi-Step Decoding for Time-Series Foundation Models

The paper introduces a dynamic multi‑step decoding approach for time‑series foundation models, arguing that the best decoding strategy varies by instance rather than being fixed. A lightweight selector network ranks candidate rollout schedules using the backbone’s hidden states, trained on historical windows and evaluated via dynamic programming to keep complexity quadratic. At deployment, multiple selectors vote to choose a schedule, defaulting to one‑shot decoding when they disagree, and the method shows statistically significant improvements in point‑forecast accuracy on the fev‑bench benchmark with frozen Chronos‑2 and Moirai‑1.1‑R‑small backbones.

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• fine-tuning
• benchmarks