Stochastic Time-Series Anomaly Detection for Lab Sensors

Develop an advanced anomaly detection framework specifically designed for scientific laboratory sensor data. Create a machine learning pipeline that can handle time-series data from multiple sensor types, implementing sophisticated statistical techniques like Gaussian mixture models, isolation forests, and adaptive threshold algorithms. The solution must provide real-time anomaly scoring, generate interpretable reports, and support automatic alerting for potential measurement irregularities.