TS-RAG把RAG用在时间序列预测上，用参考标记融合相似序列，多个基准拿SOTA，做预测的可以看看。
TS-RAG提出将检索增强生成（RAG）应用于时间序列预测，通过设计专门的参考标记来融合输入序列与检索到的相似序列。该方法旨在解决传统时序模型数据规模小、参数少、生成能力不足的问题。在多个真实世界预测基准上，TS-RAG取得了持续的最优性能。实验表明，参考标记能更有效地捕获复杂的时间动态特征。
TS-RAG: Retrieval Augmented Generation for Time Series Forecasting
While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited. Since RAG has proven effective in enhancing the capabilities of large language models by incorporating relevant external information, retrieving similar time series sequences as references might also improve accuracy in time series forecasting tasks. However, most time series models are constrained by limited training data, smaller parameter scales, and a lack of the extensive generative capabilities found in large language models. Simply concatenating reference sequences into the prompt, as done in language models, may not yield the expected results. To address these challenges, we propose a novel approach, TS-RAG, which leverages RAG to enhance forecasting performance. The framework introduces specially designed reference tokens to effectively fuse information from the input sequence with that from retrieved similar sequences, enabling a more robust capture of complex temporal dynamics. Experimental results demonstrate that TS-RAG achieves consistent state-of-the-art performance across several real-world forecasting benchmarks.