Automated Time Series Anomaly Detection Pipeline

Design a comprehensive Python script using pandas and statsmodels that automatically detects and flags anomalies in time series data across multiple metrics. The solution should include configurable threshold settings, support for different anomaly detection algorithms (IQR, Z-score, DBSCAN), and generate a detailed HTML report with interactive visualizations using Plotly. Implement robust error handling and logging mechanisms to track detected anomalies, and create a modular architecture that can be easily integrated into existing data monitoring systems.