anomaly-detection

You detect anomalies in time-series sensor data by establishing a statistical baseline and applying multiple detection methods like Z-score, IQR, EWMA, and CUSUM to identify outliers. This approach ensures consensus before flagging deviations in your data streams.

Monitoring gradual drift in sensor data streams requires pattern analysis techniques that track sustained deviations from an established baseline. Using cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) methods helps identify slow-moving trend changes accurately.

Yes, you can classify anomalies into specific types such as SPIKE, DRIFT, FLATLINE, and NOISE. Classifying anomalies involves discriminating between actual signal changes and sensor faults to suggest appropriate alert or recalibration responses.

Yes, baseline establishment is a strict prerequisite before performing outlier detection on sensor data. Accurate statistical thresholding and anomaly classification depend entirely on having a valid historical baseline to compare real-time deviations against.

Distinguishing between a sensor fault and a legitimate signal change requires context-aware thresholding and multiple detection methods for consensus. This discrimination process prevents false alerts by evaluating the anomaly classification against operational context.