CastFlow：学习用于时间序列预测的角色专用代理工作流
CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting
AI总结：
CastFlow通过动态代理框架实现多视角时间模式提取和多轮上下文特征获取，提升时间序列预测的准确性和适应性。
摘要：
近年来，大语言模型（LLMs）在时间序列预测中展现出巨大潜力。然而，大多数现有基于LLM的预测方法仍遵循静态生成范式，即单次传递直接将历史观测映射到未来值。在此范式下，预测受限于有限的时间模式提取能力、单轮上下文特征获取、单次预测生成以及缺乏集成预测的支持。为解决这些限制，本文提出CastFlow，一种动态代理预测框架，能够实现多视角时间模式提取、多轮上下文特征获取、迭代预测优化以及基于集成预测的预测。首先，CastFlow将预测过程划分为规划、行动、预测和反思，建立代理工作流。其次，该工作流由一个检索先前经验的记忆模块和一个构建诊断证据的多视角工具包支持，提供可靠的集成预测基线。第三，CastFlow采用角色专用设计，结合通用推理与专用数值预测。在此设计下，冻结的LLM保留通用推理能力，而微调的领域专用LLM则基于集成预测基线进行证据引导的数值预测，而非从头开始。为优化微调的领域专用LLM，我们进一步开发了两阶段工作流导向的训练方法，结合监督微调（SFT）和具有可验证奖励的强化学习（RLVR）。为评估CastFlow的有效性，我们在多样化的数据集上进行了广泛实验，显示其在强基线上的优越整体表现。我们希望这项工作能成为更适应和准确的时间序列预测的一步。
英文摘要：
Recently, large language models (LLMs) have shown great promise in time series forecasting. However, most existing LLM-based forecasting methods still follow a static generative paradigm that directly maps historical observations to future values in a single pass. Under this paradigm, forecasting is constrained by limited temporal pattern extraction, single-round acquisition of contextual features, one-shot forecast generation, and lack of support from ensemble forecasts. To address these limitations, in this work, we propose CastFlow, a dynamic agentic forecasting framework that enables multi-view temporal pattern extraction, multi-round contextual features acquisition, iterative forecast refinement, and forecasting with ensemble forecasts. First, CastFlow organizes the forecasting process into planning, action, forecasting, and reflection, establishing an agentic workflow. Second, this workflow is supported by a memory module that retrieves prior experience and a multi-view toolkit that constructs diagnostic evidence and provides a reliable ensemble forecast baseline. Third, CastFlow adopts a role-specialized design that combines general-purpose reasoning with specialized numerical forecasting. Under this design, a frozen LLM preserves general-purpose reasoning, while a fine-tuned domain-specific LLM performs evidence-guided numerical forecasting based on the ensemble forecast baseline, rather than from scratch. To optimize a fine-tuned domain-specific LLM, we further develop a two-stage workflow-oriented training that combines supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR). To evaluate the effectiveness of CastFlow, we conduct extensive experiments on diverse datasets and show that it achieves superior overall results against strong baselines. We hope that this work can serve as a step toward more adaptive and accurate time series forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
