让大模型像专家一样反复推敲，动态预测时间序列。
AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting
- 构建多阶段交互式推理框架，模拟人类专家思考过程。
- 在多个基准上超越主流基线，实现更精准的预测结果。
- 无需训练，适合需要可解释性与动态调整的决策场景。
时间序列预测在众多实际应用中至关重要。尽管进展显著，现有方法大多将其视为静态、单次回归问题。而人类专家通过迭代推理，结合时序特征、领域知识、案例参考与补充上下文持续优化判断。本文提出 Alphacast，一种无需训练的交互驱动型智能体推理框架，利用大语言模型实现高精度时间序列预测。该框架将预测重构为类专家流程，包含上下文准备、基于推理的生成和反思评估三个阶段，使预测从单次输出转变为多轮自主交互过程。为支持人类专家常考虑的多元视角，我们设计轻量级工具包，包括特征集、知识库、案例库和上下文池，为大模型推理提供外部支持。在多个基准上的广泛实验表明，Alphacast普遍优于代表性基线。代码已公开于 https://github.com/echo01-ai/AlphaCast。
原文摘要 · Abstract (English)
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 Alphacast, an interaction-driven agentic reasoning framework that enables accurate time series forecasting with training-free large language models. Alphacast 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 Alphacast generally outperforms representative baselines. Code is available at this repository: https://github.com/echo01-ai/AlphaCast.
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