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  1. Analysis

Anomalies

Anomaly detection in the Internet of Things (IoT) and Operational Technology (OT) is a critical process used to identify unusual patterns that do not conform to expected behavior. These anomalies can indicate various issues, such as potential system failures, security breaches, or operational inefficiencies. By leveraging anomaly detection, users can proactively monitor and maintain their IoT/OT environments, ensuring operational continuity, security, and efficiency.

Anomaly Detection Modes

Feature
Description

Streaming detection

Detect anomalies in your streaming data by using previously seen data points to determine if your latest one is an anomaly. This operation generates a model using the data points you send, and determines if the target point is an anomaly.

Batch detection

Use your time series to detect any anomalies that might exist throughout your data. This operation generates a model using your entire time series data, with each point analyzed with the same model.

Change points detection

Use your time series to detect any trend change points that exist in your data. This operation generates a model using your entire time series data, with each point analyzed with the same model.

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Last updated 8 months ago