Title: Time Series Subsequence Anomaly Detection via Graph Neural Networks. Abstract: Time series subsequence anomaly detection is an important task in a large variety of real-world applications ranging from health monitoring to AIOps, and is challenging due to complicated underlying temporal dynamics and unpredictable anomalous patterns. Firstly, how to effectively learn the temporal dependency in time series remains a challenge. Secondly, diverse and complicated anomalous subsequences as well as the lack of labels make accurate detection difficult. For example, the popular subsequence anomaly detection algorithm---time series discord---fails to handle recurring anomalies. Thirdly, many existing algorithms require a proper subsequence length for effective detection, which is difficult or impossible in practice.  In this paper, we present a novel approach to subsequence anomaly detection which combines practical heuristics of time series discords and temporal relationships with deep neural networks. By performing length selection considering multi-scale information and incorporating prior knowledge using graph neural networks, our method can adaptively learn the appropriate subsequence length as well as integrated representations from both priors and raw data favorable to anomaly detection. In particular, our graph incorporates both semantic and temporal relationships between subsequences. The experimental results demonstrate the effectiveness of the proposed algorithm, which achieves superior performance on multiple time series anomaly benchmarks in comparison with state-of-the-art algorithms.