Anomaly detection in changing data streams is tricky. Traditional methods struggle when the environment keeps switching. To tackle this, a new model was created that learns from past data but focuses on the most relevant parts. This model, called a relevance-weighted ensemble, helps detect anomalies more accurately and efficiently. Tests on real and fake data streams showed significant improvements over existing methods.