Advanced Time Series Anomaly Detection for Scientific Instrumentation

Design a sophisticated time series anomaly detection framework specifically tailored for scientific instrumentation data. Create a solution that can: 1) Implement multiple anomaly detection algorithms, 2) Support contextual and collective anomaly identification, 3) Generate interpretable anomaly reports with root cause analysis, 4) Adapt to different sensor and measurement domains. Include machine learning techniques for dynamic threshold adaptation.