Configure dynamic threshold settings

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

The Dynamic Threshold algorithm uses a statistical analysis of the 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 to produce an expected value for the metric A measurement or data point that is monitored and analyzed to detect anomalies and generate incidents., every 15 minutes. This algorithm also tracks the number of measurements that were recorded in that 15 minute window, in order to filter out noisy measurements.

The parameters in this dialog differ from Configure bounded dynamic threshold settings. In particular, the last parameter is a second probability-of-observations setting, not a minimum number of observations.

Dynamic threshold configuration includes the following settings:

Probability of Observed Value is Above %: A percentage between 0 and 100 representing how likely the observed value is normal. Values closer to 100% make the algorithm less sensitive (fewer indicators). Values closer to 0% make it more sensitive (more indicators).
Probability of Number of Observations is Above %: A percentage between 0 and 100 representing how likely the number of observations in the window is normal. This filters out periods with very few measurements. Values closer to 100% result in fewer indicators. This value should typically be below 20%. If null, this parameter will not be used to restrict the creation of indicators.
Required minimum tolerance from expected value: The metric name and unit (e.g. % or seconds) are automatically adjusted based on the metric in use. This value must be a positive number. The observed value must differ from the expected value by at least this amount for an indicator to be considered.
Probability of this number of observations is above (second instance): A second probability threshold on the number of observations. The dialog may show this parameter again. It restricts when an indicator can be created based on how many observations fall above the probability threshold.
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.
Validation: The required minimum deviation must be positive. The probability must be strictly higher than 0 and strictly lower than 100.