English(EN) TimeOmni-o1: Bidirectionally Coupling Time Series Foundation Models and LLMs for Multimodal Reasoning and Forecasting

新型AI模型双向耦合大语言模型与时间序列模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员推出TimeOmni-o1，这是一种新颖的架构，它将时间序列基础模型（TSFM）与大语言模型（LLM）进行双向耦合。这种集成通过使TSFM能够为LLM编码时间序列数据，然后LLM对文本和压缩序列表示进行推理，从而实现多模态推理和预测。LLM的推理也会反馈给TSFM，以条件化预测。为了支持这一点，创建了一个名为MMTR的新数据集，其中包含跨12个领域的超过500,000个样本，用于多模态时间序列推理和预测。 AI

影响该模型可以增强需要对结构化时间序列数据和非结构化文本进行推理的领域中的AI能力，从而改进预测和分析任务。

排序理由该集群描述了一篇详细介绍新型AI模型架构和数据集的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Ziyang Zhang, Shenyi Li, Wen Wu, Chao Zhang · 2026-11-11 05:00

TimeOmni-o1：双向耦合时间序列基础模型与大语言模型以实现多模态推理和预测

arXiv:2611.12902v1 Announce Type: new Abstract: Time series from real-world systems are often accompanied by text, such as a report on an event that affects them. Two common tasks on these series are understanding, e.g., answering questions about a series, and forecasting its fut…

本文如何被排名

报道来源 [1]

TimeOmni-o1：双向耦合时间序列基础模型与大语言模型以实现多模态推理和预测

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