![]() Instead, the preview displays the KPI scores with the thresholds calculated for the original KPI. However, if you apply that same template to other KPIs with different ranges of results, such as results in the 0-100 range versus 1000-2000, you won't see immediately useful thresholds. If you like the calculated thresholds, you can save the template and use it. If the historic data is noisy, a pattern will be difficult to detect.Īdaptive thresholds for a KPI are calculated based on the specific KPI that you're previewing. Make sure your historic data isn't too random.Because adaptive thresholding looks for historic patterns in your data, it is best to enable it for KPIs that have established baselines of data points and show a pattern or trend over time.Before you apply adaptive thresholds, it is best to decide which algorithm you want or need based on the descriptions in Create time-based static KPI thresholds in ITSI.Ĭonsider the following guidelines when deciding whether to enable adaptive thresholding for a KPI:.The itoa_admin role is assigned this capability by default. You must have the write_itsi_kpi_threshold_template capability to apply adaptive thresholds to a KPI.For instructions, see Set per-entity thresholds. ![]() If you want to perform thresholding at the per-entity level, you must use the standard thresholding procedure. You currently can't perform adaptive thresholding on a per-entity basis. The adaptive thresholds automatically recalculate on a nightly basis so that slow changes in KPI behavior don't trigger false alerts.īy dynamically calculating time-dependent thresholds, adaptive thresholding allows operations to more closely match alerts to the expected workload on an hour-by-hour basis.Īdaptive thresholds are intended to analyze and predict behavior for KPIs only. Since the shape of your data can vary dramatically, ITSI supports standard deviation, quantile, range-based, and percentage thresholds. Adaptive thresholding in IT Service Intelligence (ITSI) uses machine learning techniques to analyze historic data and determine what KPI behavior should be considered normal in your IT environment. ![]()
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