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When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting

Paper recorded by Signals 4 on 2026-09-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-21 on arXiv · recorded by Signals 4 on 2026-09-22

Category: cs.AI · 人工智能 · first seen 2026-09-22

Abstract

Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning strategies, and intervention rules inherently time-varying. Consequently, a time series agent must adapt the forecasts it produces and the orchestration policy that determines which components to trust and how to coordinate them. The deployment proce

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#20 most recent of 340 cs.AI papers we have recorded · ↑ newer: SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly f · ↓ older: Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic
Cite this page: When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting: the #20 most recent of 340 cs.AI papers we have recorded (as of 2026-09-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/when-tomorrow-becomes-today-self-evolving-policies-for-agentic-time-series-forec.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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