Configure bounded dynamic threshold settings

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

The Bounded 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 analysis can only be applied to metrics which have a bounded range of values, for example, percentages which can only be 0–100%. 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 dynamic threshold settings. In particular, the last parameter is Required minimum number of observations, and the tolerance unit follows the metric (e.g. seconds for response time, % for percentage metrics).

Bounded 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. seconds for Activity Response Time, % for percentage metrics) 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.
Required minimum number of observations: A threshold on the number of observations. If the number of observations in the window is not above this value, an indicator cannot be created, no matter how unlikely the observation is, given the learned history of the metric. Larger values make it harder to create indicators. This parameter is specific to the bounded dynamic threshold dialog.
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.