Setting controls for anomaly detection - Amazon QuickSight
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Setting controls for anomaly detection

You can find the settings for anomaly detection in the Controls section of the screen. You can open and close this section by clicking the word Controls.

Choose Controls to open the Controls sections.

The settings include the following:

  • Controls – The current settings appear at the top of the workspace. You can expand this section by choosing the double arrow icon on the right side. The following settings are available for exploring outliers generated by ML-powered anomaly detection:

    • Severity – Sets how sensitive your detector is to detected anomalies (outliers). You should expect to see more anomalies with the threshold set to Low and above, and fewer anomalies when the threshold is set to High and above. This sensitivity is determined based on standard deviations of the anomaly score generated by the RCF algorithm. The default is Medium and above.

    • Direction – The direction on the x-axis or y-axis that you want to identify as anomalous. The default is [ALL]. You can choose the following:

      • Set to Higher than expected to identify higher values as anomalies.

      • Set to Lower than expected to identify lower values as anomalies.

      • Set to [ALL] to identify all anomalous values, both high and low.

    • Minimum Delta - absolute value – Enter a custom value to use to as the absolute threshold to identify anomalies. Any amount higher than this value counts as an anomaly.

    • Minimum Delta - percentage – Enter a custom value to use to as the percentage threshold to identify anomalies. Any amount higher than this value counts as an anomaly.

    • Sort by – Choose the method that you want to apply to sorting anomalies. These are listed in preferred order on the screen. View the following list for a description of each method.

      • Weighted anomaly score – The anomaly score multiplied by the log of the absolute value of the difference between the actual value and the expected value. This score is always a positive number.

      • Anomaly score – The actual anomaly score assigned to this data point.

      • Weighted difference from expected value – (Default) The anomaly score multiplied by the difference between the actual value and the expected value.

      • Difference from expected value – The actual difference between the actual value and the expected value (actual−expected).

      • Actual value – The actual value with no formula applied.

    • Categories – One or more settings can appear at the end of the other settings. There is one for each category field that you added to the category field well. You can use category settings to limit the data that displays in the screen.