Visual Analysis for Anomaly Detection in Time-Series: A Survey

Anomaly detection for time-series denotes the detection and analysis of abnormal and unusual patterns, trends and features. Automatic methods sometimes fail to detect anomalies that are subtle, fuzzy or uncertain, while visual analysis can overcome this challenge by integrating the capability of human users and data mining approaches through visual representations of the data and visual interface. In this paper, we identify the challenges of anomaly detection, and describe the existing works of visual analysis along two categories: types of anomalies (attributes, topologies and hybrids), and anomaly detection means (direct projection, clustering and machine learning). We highlight future research directions.