虽然深度学习模型, 特别是基于变压器的架构, 在时间序列预测, 方面表现出了令人印象深刻的性能，但检索增强生成(RAG) 在该领域的应用仍然有限。由于 RAG 已被证明可以通过合并相关外部信息来有效增强大型语言模型的能力, 检索相似的时间序列序列作为参考也可能会提高时间序列预测任务的准确性。然而,大多数时间序列模型受到有限的训练数据,较小的参数规模,的限制并且缺乏大型语言模型中的广泛生成能力。像语言模型, 中那样简单地将参考序列连接到提示, 中可能不会产生预期的结果。为了应对这些挑战,，我们提出了一种新颖的方法, TS-RAG,，它利用 RAG 来增强预测性能。该框架引入了专门设计的参考标记，可以有效地将输入序列中的信息与检索到的相似序列中的信息融合,，从而能够更稳健地捕获复杂的时间动态。实验结果表明，TS-RAG 在多个现实世界的预测基准中实现了一致的最先进性能。
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.
科目: 人工智能 (cs.AI); 机器学习 (cs.LG)
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)