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Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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格兰杰-TSllm:格兰杰因果关系增强的LLM与多变量时间序列预测的剩余量子化标记器.

Jiaqi Chu1, Chengbao Liu2, Xiwei Bai2

  • 1Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.

Neural networks : the official journal of the International Neural Network Society
|December 13, 2025
PubMed
概括

本研究介绍了Granger-TSllm,这是一个使用大型语言模型 (LLM) 来进行多变量时间序列 (MTS) 预测的新框架. 它克服了数据限制,并改善了准确的MTS预测的概括性.

关键词:
对MTS进行微调的LLM.多变量时间序列预测.非线性格兰杰因果关系剩余量化代币化器的剩余量化代币化器

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