Adaptive Real-Time Anomaly Detection Architecture

Design a scalable anomaly detection system that can dynamically adapt to changing data distributions using ensemble machine learning techniques. Develop a multi-layered approach incorporating statistical methods, isolation forests, and deep learning autoencoders that can detect both point anomalies and contextual anomalies across complex, high-dimensional datasets. Include specific considerations for handling concept drift, managing false positive rates, and providing interpretable explanations for detected anomalies.