Detecting anomalies

You can detect anomalies, or outliers, in your time series data by using the built-in functions in the Analytics Service catalog.

In Analytics Service, anomaly detection is used to find patterns in your time series data that do not conform to expected behavior. Anomaly detection helps you to identify problems with your devices or assets early. For example, you might use an anomaly detector to identify that a critical device in a mechanical chain is failing before the device impacts the entire chain.

Analytics Service uses several anomaly models to detect and alert on anomaly conditions, such as the following examples:

Anomaly detectors are classified as follows:

Before you run anomaly detectors on your input data, you can run data quality checks on the input data from your sensors using a built-in function. With the quality checks, you can detect and correct the faulty data before you perform anomaly detection. For example, faulty data might relate to the quality of sensors that are used to collect data rather than to usual behavior in the environment that is being measured. For more information, see the DataQualityChecks built-in function.