Robust Time Series Anomaly Detection Framework

Architect a comprehensive time series anomaly detection system capable of handling multi-dimensional, high-frequency data streams with varying seasonality and trend characteristics. Develop a hybrid approach combining statistical methods (ARIMA, exponential smoothing), machine learning techniques (isolation forests, autoencoders), and ensemble methods. Create a modular detection pipeline that can automatically adjust detection thresholds, handle concept drift, and provide interpretable anomaly explanations.