Automated Anomaly Detection System with Machine Learning

Create a comprehensive anomaly detection framework using Python that can automatically identify statistical outliers across different data types and sources. Implement at least three detection algorithms (Isolation Forest, Local Outlier Factor, Z-Score method) with a configurable pipeline that can handle time series, numerical, and categorical data. Develop a visualization module that not only highlights anomalies but provides contextual explanations and potential root cause analysis. Include a flexible configuration system that allows domain experts to adjust sensitivity thresholds.