CastMind: An Interaction-Driven Agentic Reasoning Framework for Cognition-Inspired Time Series Forecasting

📝 Original Info

• Title: CastMind: An Interaction-Driven Agentic Reasoning Framework for Cognition-Inspired Time Series Forecasting
• ArXiv ID: 2511.08947
• Date: 2025-11-12
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (저자명 및 소속은 원 논문 또는 GitHub 저장소(https://github.com/SkyeGT/CastMind )에서 확인 가능) — **

📝 Abstract

Time series forecasting plays a crucial role in decision-making across many real-world applications. Despite substantial progress, most existing methods still treat forecasting as a static, single-pass regression problem. In contrast, human experts form predictions through iterative reasoning that integrates temporal features, domain knowledge, case-based references, and supplementary context, with continuous refinement. In this work, we propose CastMind, an interaction-driven agentic reasoning framework that enables accurate time series forecasting with training-free large language models. CastMind reformulates forecasting as an expert-like process and organizes it into a multi-stage workflow involving context preparation, reasoning-based generation, and reflective evaluation, transforming forecasting from a single-pass output into a multi-turn, autonomous interaction process. To support diverse perspectives commonly considered by human experts, we develop a lightweight toolkit comprising a feature set, a knowledge base, a case library, and a contextual pool that provides external support for LLM-based reasoning. Extensive experiments across multiple benchmarks show that CastMind generally outperforms representative baselines. Code is available at this repository: https://github.com/SkyeGT/CastMind .

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