Configure baseline settings

To configure baseline settings, click Edit for a metric that uses baselining to open the baseline configuration dialog box.

The baseline algorithm learns the seasonal (time of day or day of week) variation of the observed metric A measurement or data point that is monitored and analyzed to detect anomalies and generate incidents.. For every 15 minute period, an expected value for that metric is calculated, based on the learned metric history on the entity Things deployed in the customer environment that are needed to run the business, such as applications, devices, interfaces, and locations. being tracked. When the observed value deviates from the expected value, an indicator An observed change in a specific metric stream that is recognized as being outside of an expected model. Indicators are correlated into triggers, and one or more triggers are grouped into incidents. is created. The following parameters control how large a deviation is required for an indicator to be produced.

Baseline configuration includes the following settings:

Change Above Expected: This value should be greater than 1. If the observed value is greater than the expected value × Change Above Expected, an indicator can be produced. Larger values make it harder to create an indicator. For example, a Change Above Expected of 1.2 means that, for an expected value of 10.0, the observed value must be greater than 12 (10 × 1.2) to be an indicator. If this parameter is null, the algorithm will not create indicators where the observed value is greater than the expected value. Either Change Above or Change Below must have a value.
Change Below Expected: This value should be less than 1. If the observed value is less than the expected value × Change Below Expected, an indicator can be produced. Smaller values make it harder to create an indicator. For example, a Change Below Expected of 0.8 means that, for an expected value of 10.0, the observed value must be less than 8 (10 × 0.8) to be an indicator. If this parameter is null, the algorithm will not create indicators where the observed value is less than the expected value. Either Change Above or Change Below must have a value.
Minimum Tolerance: This parameter is similar to Change Above or Change Below Expected, but uses an absolute tolerance rather than the relative tolerance of Change Above or Change Below Expected. Larger values make it harder to create an indicator. For example, a Minimum Tolerance value of 5.0 means that, for an expected value of 10.0, the observed value must be greater than 15 (10 + 5.0) or less than 5 (10 - 5.0) to be an indicator. If this parameter is null, it will not be used to restrict the creation of indicators.
N of M parameters: These parameters restrict indicator production to only those times when the algorithm has seen anomalous behavior in N out of the last M observations.
Metric name and unit: The metric name and the unit of the required minimum deviation are automatically adjusted based on the metric unit in use.
Granularity: The granularity setting (for example, 15 mins) is the time interval used for baselining. Set it to match the interval that Analytics uses for this metric.
Validation: All numeric values must be positive numbers.