Real-Time Intelligence tutorial part 7: Detect anomalies on an Eventhouse table

Note

This tutorial is part of a series. For the previous section, see: Real-Time Intelligence tutorial part 6: Create a Real-Time Dashboard.

Anomaly detection is a feature of Real-Time Intelligence that allows you to identify unusual patterns in your data. In this part of the tutorial, you learn how to create an 'Anomaly detector' item on your workspace to detect anomalies in the number of empty docks at a station.

Detect anomalies on an Eventhouse table

From the left navigation bar, select Real-Time to open the Real-Time hub.
Under All data streams select the eventhouse table TransformedData you created in the previous tutorial. The table details page opens. Select Detect anomalies from the top menu.
Enter BikeAnomaliesconfiguration as Name.
Under Save to, select Create detector.
Select the workspace in which you want to create the anomaly detector item, enter BikeAnomalies. Then select Create.
In the Select attributes section, choose the following options:

Field Value

Value to watch No_Empty_Docks

Group by Street

Timestamp Timestamp
Select Run analysis.

Important

Analysis typically takes up to 4 minutes depending on your data size and can run for up to 30 minutes. You can navigate away from the page and check back in when the analysis is complete.

Note

Ensure your Eventhouse table contains sufficient historical data to improve model recommendations and anomaly detection accuracy. For example, datasets with one data point per day require a few months of data, while datasets with one data point per second might only need a few days.
When analysis is complete, anomalies along with tabular data are displayed on the right.

Note

Play around with the Detection model under Customize detection section and Timestamp above the Detector results pane. More data might increase anomaly detection accuracy.
Select Save.