Data Driven Anomaly Detection

Data-driven anomaly detection aims to automatically identify unusual patterns in data streams from diverse sources, improving efficiency and reliability across various applications. Current research emphasizes developing robust and interpretable models, including deep learning architectures like transformers and employing techniques such as self-supervised pre-training and two-stage approaches to handle heterogeneous data. This field is crucial for enhancing the safety and efficiency of systems ranging from industrial processes and satellite operations to transportation infrastructure, with a growing focus on explainability to foster trust and facilitate actionable insights.