Dynamic Time Series Anomaly Detection Pipeline

Design a comprehensive Python anomaly detection system using pandas and numpy that can automatically identify statistical outliers across time series data. The solution should handle multiple data sources, support configurable sensitivity thresholds, and generate automated alert mechanisms. Implement both statistical (Z-score, IQR) and machine learning-based (Isolation Forest, Local Outlier Factor) detection methods. Include visualization capabilities using Plotly and a modular architecture that allows easy integration with existing data infrastructures.