Smart Detection and Fault Diagnosis Technology
Furong CHEN, Chen XIONG, Ting LI, Chao ZHONG, Zhaoyang MA, Da LI, Jing WANG
Time series anomaly detection, however, faces numerous challenges due to the complexity of data characteristics, algorithmic requirements, and diverse application scenarios. To address this, this paper presents a comprehensive survey of time series anomaly detection. First, the paper systematically analyzes the complexity and challenges of time series anomaly detection tasks from three dimensions: data characteristics, algorithm requirements, and application scenarios. Second, it categorizes anomalies in time series into point anomalies, subsequence anomalies, and inter-variable correlation anomalies, providing a detailed exposition of the definitions and detection methods for each type. Third, the paper reviews and analyzes the use of traditional statistical methods, machine learning techniques, and deep learning approaches in time series anomaly detection, evaluating their applicability and limitations. Subsequently, it compiles widely used time series anomaly detection datasets, analyzing the application scenarios and unique features of each dataset. Finally, it discusses future research directions in time series anomaly detection from five perspectives: anomaly localization, anomaly classification, precursor forecasting, interpretability, and integration with large-scale models. The review highlights that current challenges, including data scarcity, anomaly diversity, and concept drift, remain unresolved. Future anomaly detection research is expected to evolve toward more granular tasks such as anomaly localization and prediction.