Anomaly detection in non-stationary data requires methods that adapt to changing statistical patterns over time. Non-stationary data, such as time series with trends, seasonal variations, or sudden shifts, breaks the assumption that data distributions remain static. Traditional anomaly detectors trained on historical data often fail in these scenarios because they cannot account for evolving patterns. To address this, adaptive models like online learning algorithms incrementally update their parameters as new data arrives. For example, a moving average or exponential smoothing technique can adjust to recent trends, while ensemble methods combine multiple models weighted by their recent performance. This allows the system to stay relevant without requiring full retraining, making it practical for streaming data applications like monitoring server traffic or sensor readings.
Handling concept drift—changes in the relationship between input features and anomalies—is another critical challenge. Techniques like drift detection algorithms (e.g., ADWIN or Page-Hinkley tests) identify when data patterns shift, triggering model updates or retraining. Dynamic thresholding adjusts anomaly criteria based on recent data windows instead of fixed historical baselines. For instance, in fraud detection, a system might recalculate normal spending behavior every week to account for seasonal shopping trends. Sliding window approaches focus on the most recent data (e.g., the last 24 hours of network logs) to prioritize current patterns. These strategies ensure the model remains aligned with the latest data distribution, reducing false positives caused by outdated assumptions.
Feature engineering and preprocessing also play a key role. Differencing—subtracting consecutive data points—can remove trends from time series data, making it stationary and easier to model. Rolling statistics (e.g., 30-day mean and variance) capture local patterns, enabling anomaly detection relative to recent behavior. In IoT applications, temperature sensors might use rolling z-scores to flag deviations from the past hour’s baseline. Additionally, models like ARIMA or Prophet explicitly handle non-stationarity through built-in differencing and trend components. For developers, combining these techniques—adaptive thresholds, concept drift handling, and stationarity-focused preprocessing—ensures robust anomaly detection even as data evolves. Testing with time-split validation and monitoring model performance over time are essential to validate these approaches.