面向工业时间序列预测的人在回路自主智能体

中文摘要 AI 辅助

实际场景中的时间序列预测极少是单次模型调用就能完成的：从业者必须完成任务制定、数据与模型的连接、领域知识的融入、预测合理性评估以及不确定性的传达。专用预测模型能提供精准的数值预测，但通常在固定流程中运行；而通用大语言模型（LLM）智能体往往缺乏预测领域特有的校验、约束和停止规则。我们提出CastClaw，这是一个通过面向预测的框架工程构建的人在回路自主预测系统。CastClaw在一个运行时环境中整合了数据、专用模型、分析工具、用户输入以及带版本的执行记录。用户可用自然语言指定预测目标、预测时域、约束条件和假设。从提供的或模型生成的预测结果出发，CastClaw会检查时间模式和用户约束；当证据不足时，它会检索上下文、运行分析或其他模型，或向用户发起询问。之后，它会在明确的停止条件下保留、修正或升级结果。输出包含最终预测结果以及记录输入、证据、操作和修正的执行报告。在包含5个数据集的电价场景中，CastClaw在16个基线模型中取得了最低的点估计均方误差（MSE）和平均绝对误差（MAE）；Nord Pool案例展示了可检查的工作流程；CastClaw还在2026年1月至6月的中国省级电力负荷数据上完成了离线验证。

英文摘要

Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate domain expertise, assess prediction plausibility, and communicate uncertainty. Specialized forecasting models provide strong numerical predictions but usually operate in fixed pipelines, while general-purpose large language model (LLM) agents often lack forecasting-specific checks, constraints, and stopping rules. We present CastClaw, a human-in-the-loop autonomous forecasting system built through forecasting-oriented harness engineering. CastClaw connects data, specialized models, analytical tools, user input, and a versioned execution record in one runtime. Users specify the target, horizon, constraints, and hypotheses in natural language. Starting from a supplied or model-generated forecast, CastClaw checks temporal patterns and user constraints; when evidence is missing, it retrieves context, runs an analysis or another model, or asks the user. It then keeps, revises, or escalates the result under explicit stopping conditions. The output contains the final forecast and an execution report recording inputs, evidence, actions, and revisions. In this five-dataset electricity-price setting, CastClaw reports the lowest point-estimate MSE and MAE among 16 baselines. A Nord Pool case demonstrates the inspectable workflow. CastClaw was also validated offline on provincial electricity-load data from North China covering January--June 2026.

发表机构

• University of Science and Technology of China（中国科学技术大学）

机构由 AI 辅助整理，请以论文原文为准。