anomalydetection

Anomaly detection is the process of identifying patterns that do not conform to expected behavior, often called anomalies, outliers, or novelties. It seeks to flag observations that may indicate errors, fraud, or rare but important events. Anomalies can be point, contextual, or collective, and the appropriate definition varies by domain.

Approaches are typically categorized by supervision. Unsupervised methods assume that normal data are prevalent and anomalies are rare; semi-supervised methods learn from mostly normal data; supervised methods use labeled anomalies. Common techniques include statistical methods (z-scores, robust statistics), distance- and density-based methods (k-NN, Local Outlier Factor), probabilistic models, one-class classifiers, and reconstruction-based methods (PCA, autoencoders). For time-series data, forecasting and sequence models (ARIMA, LSTM) are used.

Evaluation relies on metrics such as precision, recall, F1, and ROC or PR curves. Because anomalies are rare, class imbalance and the cost of false positives are important considerations. Datasets often require careful labeling, and performance can drift over time due to changing data distributions or concept drift.

Applications include cybersecurity (intrusion detection), fraud detection, industrial monitoring and predictive maintenance, healthcare, and quality control. Anomaly detection is also used in image, video, and sensor data analysis.

Challenges include data quality and labeling, interpretability of results, real-time processing, privacy concerns, and adapting to evolving data distributions.