Detecting anomalies involves various methods, each with its own strengths:
1. Graphical Methods: Techniques such as box plots, scatter plots, and histograms can visually highlight outliers. 2. Statistical Tests: Methods like the Grubbs' test, Dixon's Q test, and the Z-score method can quantitatively identify outliers. 3. Machine Learning Algorithms: Advanced algorithms such as Isolation Forest and One-Class SVM are increasingly used for anomaly detection in large datasets.
