What is the relationship between anomaly detection and forecasting?

Anomaly detection and forecasting are closely related techniques often used together in data analysis, but they serve different purposes. Anomaly detection focuses on identifying unexpected patterns or outliers in data, while forecasting aims to predict future values based on historical trends. The connection lies in how forecasting models provide a baseline expectation, and anomalies are detected as deviations from that baseline. For example, a forecasting model might predict server traffic for the next hour, and any significant deviation from that prediction could trigger an anomaly alert. This interplay allows systems to detect issues early by comparing real-time data against predicted values.

A practical example is in network monitoring. Suppose a forecasting model predicts bandwidth usage for a web service based on daily patterns. If actual usage suddenly spikes to 200% of the forecasted value, anomaly detection algorithms flag this as a potential issue, such as a DDoS attack or a configuration error. Similarly, in energy consumption analysis, a utility company might forecast household electricity usage for a given day. If a smart meter reports consumption far below the forecast (e.g., during peak hours), it could indicate a malfunction or tampering. In both cases, forecasting establishes the “normal” range, and anomaly detection identifies outliers that require investigation.

Developers integrating these techniques should consider their interdependence. For instance, inaccurate forecasts can lead to false positives in anomaly detection. If a forecasting model fails to account for seasonal trends (e.g., holiday sales in e-commerce), anomalies might be incorrectly flagged during expected high-traffic periods. To address this, teams often refine forecasting models using anomaly detection results—for example, excluding anomalous data points (like server outages) when retraining a forecasting model to improve accuracy. Tools like Facebook Prophet or libraries like scikit-learn provide built-in support for handling outliers in time-series forecasting, streamlining this integration. By combining these approaches, developers can build systems that not only predict future states but also dynamically adapt to unexpected events.