Complex Time Series Anomaly Detection Strategy

Create a robust time series anomaly detection framework that can handle multiple data streams with varying periodicity and noise levels. Implement a hybrid approach combining statistical methods (ARIMA, exponential smoothing) with machine learning techniques like isolation forests and deep learning autoencoders. Provide a comprehensive evaluation methodology that includes false positive/negative rate analysis and dynamic threshold adjustment mechanisms.