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English(EN) OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning

新的LLM研究以多模态和跨语言模型为目标，用于金融预测 · 跟踪6个来源

作者 PulseAugur 编辑部 · [6 个来源] ·

研究人员正在开发用于金融预测和时间序列分析的高级语言模型。OpenTSLM TeeMoE通过整合多个专业专家，统一了预测、上下文预测和时间推理。DualCast使用双路径框架来预测金融时间序列，并在预测时整合新闻。另一种方法NMIXX，将现有的编码器应用于跨语言金融文本分析，提高了金融相关性，但略微降低了通用领域相关性。此外，还创建了一个新的基准MM-FinEval，用于评估多模态LLM在金融任务中的表现，使用财报电话会议中的文本、音频和视觉数据。最后，一项研究表明，像Qwen3-4B这样的预训练语言模型可以显著提高其股价预测能力。 AI

影响 这些在金融领域LLM方面的进展可能带来更复杂、更准确的市场预测和分析工具。

排序理由 该集群包含多篇研究论文，详细介绍了用于金融预测和时间序列分析的新模型、基准和方法。

在 arXiv cs.LG 阅读 →

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新的LLM研究以多模态和跨语言模型为目标，用于金融预测 · 跟踪6个来源

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该集群包含多篇研究论文，详细介绍了用于金融预测和时间序列分析的新模型、基准和方法。
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报道来源 [6]

  1. arXiv cs.LG TIER_1 English(EN) · Tony Chen, Timo Stoffregen, Maxwell Xu, Thomas Kaar, Martin Maritsch, Geremia Pompei, Nicolas Zumarraga, Robert Jakob, Paul Schmiedmayer, Patrick Langer, Juncheng Liu ·

    OpenTSLM TeeMoE：一个统一的时间序列语言模型，用于预测、上下文预测和推理

    arXiv:2609.40265v1 Announce Type: new Abstract: Real-world time-series applications increasingly require models that can handle time series forecasting, context-conditioned prediction, and language-based temporal reasoning. Yet current time-series foundation models remain fragmen…

  2. arXiv cs.AI TIER_1 English(EN) · Sujung Kim, Seung Hwan Cho, Sangjin Park, Young-Min Kim ·

    用于股票回报预测忠实大语言模型叙述的推理外化

    arXiv:2609.38869v1 Announce Type: new Abstract: In finance, interpreting machine learning predictions is essential, yet the numerical outputs of explainable AI can be difficult for non-experts to understand. While large language models (LLMs) can translate these outputs into natu…

  3. arXiv cs.AI TIER_1 English(EN) · Wentao Zhao, Hongqiang Wu, Shanghang Liu, Zhaochen Zan, Yu Zhang, Biqing Huang ·

    DualCast：用于双模态金融时间序列预测的双路径语言模型

    arXiv:2609.38197v1 Announce Type: cross Abstract: Financial time-series forecasting must capture price dynamics across heterogeneous assets while incorporating news available at prediction time. We introduce DualCast, a dual-path framework that extends a frozen language model wit…

  4. arXiv cs.AI TIER_1 English(EN) · Dong Shu, Yanguang Liu, Huopu Zhang, Saisai Hu, Haiyan Zhao, Hekun Huang, Mengnan Du ·

    MM-FinEval：面向真实世界金融预测的多任务多模态基准

    arXiv:2609.38523v1 Announce Type: cross Abstract: Financial forecasting from earnings conference calls requires models to reason over complex corporate disclosures, market expectations, and subtle communication signals. However, existing financial benchmarks are often limited to …

  5. arXiv cs.AI TIER_1 English(EN) · Hanwool Lee, Sara Yu, Yewon Hwang, Jonghyun Choi, Heejae Ahn, Sungbum Jung, Youngjae Yu ·

    NMIXX: 用于金融跨语言探索的领域自适应神经嵌入

    arXiv:2507.09601v3 Announce Type: replace-cross Abstract: Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMIXX adapts existing encoders through 18.8k source-linked triplets: paraphrases an…

  6. arXiv cs.CL TIER_1 English(EN) · Jiacheng Guo, Suozhi Huang, Shuzhen Li, Yunlong Gao, Zerui Cheng, Jason Ge, Shushu Liang, Zihao Li, Hao Lu, Ming Yin, Shilong Liu, Jiashuo Liu, Xu Kuang, Mengdi Wang ·

    语言模型能否学会预测股票价格

    arXiv:2609.36914v1 Announce Type: new Abstract: Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning, software engineering, and computer use. However, whether the same approach can i…

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