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English(EN) ReasonCast: Towards Explainable Time Series Forecasting with Reasoning

新方法将时间序列模型与大型语言模型对齐，以增强推理和预测能力

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了新的方法，将时间序列基础模型（TSFM）与大型语言模型（LLM）集成，以增强推理能力。TS-Reasoner通过两阶段训练过程，专注于将TSFM的潜在表示与LLM的文本输入对齐，与现有模型相比，表现出卓越的性能和数据效率。另一方面，ReasonCast旨在创建一个统一模型，在单次自回归过程中同时生成数值预测和可验证的因果推理链，在预测准确性方面优于LLM和TS模型。 AI

影响 这些进展可能带来更复杂的AI系统，使其能够理解和推理跨各行业的复杂时间序列数据。

排序理由 两篇研究论文介绍了将时间序列模型与LLM集成的新颖方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新方法将时间序列模型与大型语言模型对齐，以增强推理和预测能力

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两篇研究论文介绍了将时间序列模型与LLM集成的新颖方法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Xiaoyu Tao, Mingyue Cheng, Bokai Pan, Chuang Jiang, Huanjian Zhang, Tian Gao, Yaguo Liu, Qi Liu, Enhong Chen ·

    CastFSR：一种用于上下文感知时间序列预测的快速-慢速-反射代理推理框架

    arXiv:2608.03031v1 Announce Type: new Abstract: Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextual features. Recent advances in large language model…

  2. arXiv cs.CL TIER_1 English(EN) · Fangxu Yu, Hongyu Zhao, Tianyi Zhou ·

    TS-Reasoner：使时间序列基础模型与LLM推理对齐

    arXiv:2510.03519v2 Announce Type: replace Abstract: Time series reasoning is crucial to decision-making in diverse domains, including finance, energy, and scientific discovery. While existing time series foundation models (TSFMs) can capture low-level dynamic patterns and provide…

  3. arXiv cs.LG TIER_1 English(EN) · Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn ·

    ReasonCast：迈向可解释的带推理的时间序列预测

    arXiv:2608.01875v1 Announce Type: cross Abstract: Most time series (TS) models are specialized for a single task, either understanding (i.e., returning text answers about a TS) or generation (i.e., returning a numeric forecast). Only recently have unified models begun to handle t…

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