Anomaly detection in data streams is important for spotting unusual events in real-time data. Traditional methods struggle with changing environments, leading to errors. To address this, a new model was created that learns from past data, giving more weight to relevant time periods. This model, called a relevance-weighted ensemble, improves accuracy without being too complex. Tests on real and fake data streams showed significant enhancements over existing methods.