RATFM：用于异常检测的检索增强时间序列基础模型
RATFM: Retrieval-augmented Time Series Foundation Model for Anomaly Detection
AI总结：
本文提出检索增强时间序列基础模型RATFM，通过引入目标域正常示例实现测试时自适应异常检测，在避免领域微调的同时取得与领域内微调相当的性能。
摘要：
受大语言模型（LLMs）在自然语言处理中成功的启发，近期研究探索了时间序列基础模型的构建，并将其应用于预测、分类和异常检测等任务。然而，这些模型在不同领域和任务中的表现存在差异。在基于LLM的方法中，由于重新训练成本高昂，利用基于示例的提示进行测试时自适应已变得普遍。在本研究所关注的异常检测背景下，提供来自目标领域的正常示例同样可能有效。然而，时间序列基础模型并不会自然获得解释或利用示例与指令的能力，因为训练所用时间序列数据的性质并不鼓励此类能力。为解决这一局限，我们提出了一种检索增强时间序列基础模型（RATFM），使预训练的时间序列基础模型能够结合示例进行测试时自适应。我们证明，RATFM在避免依赖领域的微调的同时，取得了与领域内微调相当的性能。在包含九个领域的多领域数据集UCR Anomaly Archive上的实验证实了所提方法的有效性。
英文摘要：
Inspired by the success of large language models (LLMs) in natural language processing, recent research has explored the building of time series foundation models and applied them to tasks such as forecasting, classification, and anomaly detection. However, their performances vary between different domains and tasks. In LLM-based approaches, test-time adaptation using example-based prompting has become common, owing to the high cost of retraining. In the context of anomaly detection, which is the focus of this study, providing normal examples from the target domain can also be effective. However, time series foundation models do not naturally acquire the ability to interpret or utilize examples or instructions, because the nature of time series data used during training does not encourage such capabilities. To address this limitation, we propose a retrieval augmented time series foundation model (RATFM), which enables pretrained time series foundation models to incorporate examples of test-time adaptation. We show that RATFM achieves a performance comparable to that of in-domain fine-tuning while avoiding domain-dependent fine-tuning. Experiments on the UCR Anomaly Archive, a multi-domain dataset including nine domains, confirms the effectiveness of the proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
