深層学習による時系列異常検知手法の課題点

抄録

In this paper, we discuss the current issues in time-series anomaly detection using deep learning and propose directions for their resolution. Instead of focusing solely on anomaly detection methods, we emphasize the importance of clarifying the problem setting from four perspectives: "system," "failure modes," "available data," and "operations." Additionally, as a potential future development, we suggest research directions that utilize tools such as Large Language Models (LLMs) against reliability design documentation to support operations and problem definition surrounding anomaly detection.