The Z-score algorithm is a simple and efficient method for detecting anomalies in data streams. It uses statistical properties like the mean and standard deviation within a fixed-size sliding window to determine how far a data point deviates from the norm. Points with high deviations, based on a predefined threshold, are flagged as anomalies.
- Sliding Window: A fixed-size window of recent data points is maintained (e.g., 50 points).
- Z-Score Calculation: The Z-score is calculated for each new point:
[
Z = \frac{(X - \mu)}{\sigma}
]
- ( X ): Current data point.
- ( \mu ): Mean of the data in the window.
- ( \sigma ): Standard deviation of the data in the window.
- Anomaly Detection: If the absolute value of the Z-score exceeds a specified threshold (e.g., 3), the point is flagged as an anomaly.
- Space Complexity: ( O(n) ), where ( n ) is the window size.
- Time Complexity: ( O(n) ) per point, primarily for computing the mean and standard deviation.
- Quick Response: The algorithm processes each point as it arrives, making it effective for real-time applications.
- Low Overhead: Constant memory and computation requirements make it well-suited for continuous data streams.
- The Z-score method is highly effective at detecting sudden, large deviations in data (e.g., spikes).
- By adjusting the window size and threshold, it can balance sensitivity and stability, making it adaptable to various anomaly detection tasks.