ML-powered anomaly detection for outliers - Amazon QuickSight
Computation outputs

ML-powered anomaly detection for outliers

The ML-powered anomaly detection computation searches your data for outliers. For example, you can detect the top three outliers for total sales on January 3, 2019. If you enable contribution analysis, you can also detect the key drivers for each outlier.

To use this function, you need at least one dimension in the Time field well, at least one measure in the Values field well, and at least one dimension in the Categories field well. The configuration screen provides an option to analyze the contribution of other fields as key drivers, even if those fields aren't in the field wells.

For more information, see Detecting outliers with ML-powered anomaly detection.

Note

You can't add ML-powered anomaly detection to another computation, and you can't add another computation to an anomaly detection.

Computation outputs

Each function generates a set of output parameters. You can add these outputs to the autonarrative to customize what it displays. You can also add your own custom text.

To locate the output parameters, open the Computations tab on the right, and locate the computation that you want to use. The names of the computations come from the name that you provide when you create the insight. Choose the output parameter by clicking on it only once. If you click twice, you add the same output twice. You can use items displayed in bold monospace font following in the narrative.

  • timeField – From the Time field well.

    • name – The formatted display name of the field.

    • timeGranularity – The time field granularity (DAY, YEAR, and so on).

  • categoryFields – From the Categories field well.

    • name – The formatted display name of the field.

  • metricField – From the Values field well.

    • name – The formatted display name of the field.

    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).

  • itemsCount – The number of items included in this computation.

  • items – Anomalous items.

    • timeValue – The values in the date dimension.

      • value – The date/time field at the point of the anomaly (outlier).

      • formattedValue – The formatted value in the date/time field at the point of the anomaly.

    • categoryName – The actual name of the category (cat1, cat2, and so on).

    • direction – The direction on the x-axis or y-axis that's identified as anomalous: HIGH or LOW. HIGH means "higher than expected." LOW means "lower than expected."

      When iterating on items, AnomalyDetection.items[index].direction can contain either HIGH or LOW. For example, AnomalyDetection.items[index].direction='HIGH' or AnomalyDetection.items[index].direction=LOW. AnomalyDetection.direction can have an empty string for ALL. An example is AnomalyDetection.direction=''.

    • actualValue – The metric's actual value at the point of the anomaly or outlier.

      • value – The raw value.

      • formattedValue – The value formatted by the metric field.

      • formattedAbsoluteValue – The absolute value formatted by the metric field.

    • expectedValue – The metric's expected value at the point of the anomaly (outlier).

      • value – The raw value.

      • formattedValue – The value formatted by the metric field.

      • formattedAbsoluteValue – The absolute value formatted by the metric field.