Advanced Time Series Anomaly Detection Pipeline

Design a comprehensive Python script using pandas and scikit-learn to create an automated anomaly detection system for time series data. The solution should handle multiple data streams, implement at least three different anomaly detection algorithms (isolation forest, local outlier factor, and statistical z-score method), and generate a detailed reporting mechanism with visualization using plotly. Include robust error handling for various input data formats and demonstrate how the pipeline can be modularized for easy integration into existing data infrastructure.