Anomaly Detection in Time-Series Data

Develop a sophisticated SQL-based anomaly detection system using statistical methods and machine learning techniques. Create a query framework that identifies statistical outliers across multiple dimensions, calculates z-scores, implements adaptive thresholding, and generates comprehensive anomaly reports. The solution should support multiple detection algorithms, handle seasonality, and provide configurable sensitivity levels for different data types and distributions.