Anomaly detection identifies unusual patterns in data that deviate from expected behavior. Common techniques include statistical methods, machine learning models, and proximity-based approaches. Each method has distinct strengths and is chosen based on data type, context, and the nature of anomalies being detected.
Statistical methods are foundational for anomaly detection. These techniques rely on mathematical models to define “normal” behavior and flag deviations. For example, Z-score analysis measures how many standard deviations a data point is from the mean, with values beyond a threshold (e.g., ±3) marked as anomalies. Time-series methods like moving averages or exponential smoothing detect spikes or drops in sequential data. A retailer might use statistical process control to monitor daily sales; if sales suddenly drop by 50% without a clear reason, the system flags it. These methods are simple to implement but assume data follows a known distribution, which may not hold for complex datasets.
Machine learning (ML) models handle more nuanced scenarios. Supervised learning trains classifiers like Random Forests or SVMs on labeled data (normal vs. anomalous) to predict anomalies. For unlabeled data, unsupervised methods like Isolation Forests isolate anomalies by randomly partitioning data—fewer splits mean higher anomaly likelihood. Autoencoders, a type of neural network, learn compressed data representations and flag inputs with high reconstruction errors. For instance, in network security, an autoencoder trained on normal traffic patterns could detect unusual packet sizes or frequencies. ML models adapt to complex patterns but require careful tuning and computational resources.
Proximity-based methods measure similarity between data points. Clustering algorithms like k-means group similar data, treating points far from cluster centers as anomalies. DBSCAN identifies outliers as points in low-density regions. Distance-based techniques like k-NN compute the average distance to the nearest neighbors; unusually distant points are anomalies. In fraud detection, a bank might use k-NN to compare transaction features (amount, location) against historical data. Proximity methods work well with high-dimensional data but struggle with scalability in large datasets. Choosing the right technique depends on balancing accuracy, interpretability, and computational efficiency for the specific use case.