Time series forecasting plays a crucial role in data mining, driving rapid advancements across numerous industries. With the emergence of large models, time series foundation models (TSFMs) have exhibited remarkable generalization capabilities, such as zero-shot learning, through large-scale pre-training. Meanwhile, Retrieval-Augmented Generation (RAG) methods have been widely employed to enhance the performance of foundation models on unseen data, allowing models to access to external knowledge. In this paper, we introduce TimeRAF, a Retrieval-Augmented Forecasting model that enhance zero-shot time series forecasting through retrieval-augmented techniques. We develop customized time series knowledge bases that are tailored to the specific forecasting tasks. TimeRAF employs an end-to-end learnable retriever to extract valuable information from the knowledge base. Additionally, we propose Channel Prompting for knowledge integration, which effectively extracts relevant information from the retrieved knowledge along the channel dimension. Extensive experiments demonstrate the effectiveness of our model, showing significant improvement across various domains and datasets.

翻译：时间序列预测在数据挖掘中扮演着关键角色，并推动着众多行业的快速发展。随着大模型的出现，时间序列基础模型通过大规模预训练展现出卓越的泛化能力，例如零样本学习。与此同时，检索增强生成方法已被广泛用于提升基础模型在未见数据上的性能，使模型能够访问外部知识。本文提出TimeRAF，一种检索增强预测模型，通过检索增强技术提升零样本时间序列预测能力。我们构建了针对特定预测任务定制的时序知识库。TimeRAF采用端到端可学习的检索器从知识库中提取有价值信息。此外，我们提出了面向知识整合的通道提示方法，能够沿通道维度从检索知识中有效提取相关信息。大量实验证明了我们模型的有效性，其在多个领域和数据集上均显示出显著性能提升。