Advanced Time Series Anomaly Detection Framework

Design a comprehensive anomaly detection pipeline for multivariate time series data that can handle complex, non-linear patterns across different data streams. The solution should incorporate at least three advanced detection algorithms (e.g., Isolation Forest, LSTM Autoencoders, Statistical Process Control), with configurable sensitivity thresholds. Include mechanisms for real-time alerting, root cause analysis, and automatic feature importance ranking. Provide a modular architecture that can be easily integrated into existing data infrastructure and supports both batch and streaming data processing.