Comprehensive Anomaly and Outlier Detection Framework

Design a multi-layered anomaly detection system capable of identifying complex, context-dependent outliers across high-dimensional datasets. Develop an ensemble approach combining statistical methods, machine learning techniques, and domain-specific knowledge to detect both point and contextual anomalies. Include advanced strategies for managing false positive rates, providing interpretable explanations, and adapting to changing data distributions.