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English(EN) A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

新型自主代理CastClaw提升工业时间序列预测能力

研究人员开发了CastClaw,一个用于工业时间序列预测的人机协同自主代理。该系统将数据、专业预测模型、分析工具和用户输入整合到一个统一的运行时环境中。CastClaw允许用户用自然语言指定预测任务、约束和假设,并能迭代地检查时间模式和用户定义的规则,在需要时检索上下文或请求澄清。在电力价格数据集上的评估中,CastClaw在16种基线方法中实现了最低的点估计MSE和MAE,并且在Nord Pool数据上的案例研究展示了其可检查的工作流程。 AI

影响 这种人机协同代理有望提高依赖时间序列数据的行业的预测系统的准确性和可解释性。

排序理由 该集群描述了一篇在arXiv上发表的研究论文,详细介绍了一种用于时间序列预测的新型自主代理。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新型自主代理CastClaw提升工业时间序列预测能力

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该集群描述了一篇在arXiv上发表的研究论文,详细介绍了一种用于时间序列预测的新型自主代理。[lever_c_demoted from research: ic=1 ai=1.0]
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paper, product
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xiaoyu Tao, Mingyue Cheng, Ze Guo, Bokai Pan, Qi Liu, Shijin Wang, Enhong Chen ·

    面向行业时间序列预测的人机协同自主代理

    arXiv:2608.30976v1 Announce Type: new Abstract: 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. Specia…