Anomaly detection and change detection are both techniques to identify deviations in data, but they address different problems. Anomaly detection focuses on identifying rare or unexpected data points that differ significantly from the norm. These anomalies are often isolated events, like a sudden spike in server CPU usage. Change detection, on the other hand, identifies shifts in the underlying patterns or behavior of a system over time, such as a gradual increase in network latency. While both deal with deviations, anomaly detection targets outliers, whereas change detection tracks systemic transitions.
Anomaly detection is commonly used in scenarios where individual data points need scrutiny. For example, in fraud detection, a credit card transaction deviating from a user’s typical spending pattern might be flagged as anomalous. Techniques like statistical methods (e.g., Z-score), clustering (e.g., DBSCAN), or machine learning models (e.g., autoencoders) are often employed. Developers might implement these by setting thresholds or training models on normal data to detect outliers. A practical use case is monitoring server logs: a single request causing a 10x latency spike could be an anomaly requiring investigation. The emphasis is on identifying irregular instances, not necessarily long-term trends.
Change detection, by contrast, analyzes sequences of data to pinpoint when a system’s behavior shifts. For instance, a manufacturing sensor might detect a gradual drift in temperature readings, signaling equipment degradation. Methods like CUSUM (Cumulative Sum) control charts, Bayesian change point detection, or time-series segmentation (e.g., Prophet) are typical tools. Developers might apply these to track metrics like application error rates over weeks to detect if a recent update introduced instability. Unlike anomaly detection, which focuses on individual points, change detection evaluates trends. A single outlier won’t trigger a change alert, but a sustained deviation (e.g., a 20% drop in daily active users) would. In practice, these techniques often complement each other: anomaly detection can highlight symptoms, while change detection diagnoses underlying shifts.