Time-Series Metabolomics Data Normalization Pipeline

Develop a PostgreSQL stored procedure that performs advanced normalization and outlier detection for high-dimensional metabolomics time-series data. The procedure must handle batch effects, implement multiple statistical normalization techniques (z-score, quantile, and robust scaling), and generate a comprehensive statistical report. Include error handling for different data distribution types and create a mechanism to track normalization parameters for reproducibility.