用检索增强生成提升大模型时间序列预测能力
TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation
- 构建历史序列知识库，通过动态时间规整检索相似模式
- 平均提升原模型预测准确率2.97%
- 适合需要高泛化性的时序预测场景
尽管大型语言模型（LLMs）为时间序列预测带来了新机遇，但现有基于LLM的解决方案需大量训练且泛化能力有限。针对这一挑战，我们提出TimeRAG框架，将检索增强生成（RAG）引入时间序列预测的LLM中。该框架从历史序列构建时间序列知识库，利用动态时间规整（DTW）度量查询序列与知识库中参考序列的模式相似性，检索出相似序列，并将其与预测查询共同构成文本提示输入给时间序列预测LLM。在多个领域数据集上的实验表明，RAG的引入使原模型预测准确率平均提升2.97%。
原文摘要 · Abstract (English)
Although the rise of large language models (LLMs) has introduced new opportunities for time series forecasting, existing LLM-based solutions require excessive training and exhibit limited transferability. In view of these challenges, we propose TimeRAG, a framework that incorporates Retrieval-Augmented Generation (RAG) into time series forecasting LLMs, which constructs a time series knowledge base from historical sequences, retrieves reference sequences from the knowledge base that exhibit similar patterns to the query sequence measured by Dynamic Time Warping (DTW), and combines these reference sequences and the prediction query as a textual prompt to the time series forecasting LLM. Experiments on datasets from various domains show that the integration of RAG improved the prediction accuracy of the original model by 2.97% on average.
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