--- title: Create a MetricBase model trigger description: Model triggers execute when the time-series data deviates from expected values. locale: en-US canonical_url: https://www.servicenow.com/docs/r/australia/servicenow-platform/metricbase/create-metricbase-model-trigger.html release: australia product: MetricBase classification: metricbase topic_type: task last_updated: "2026-03-12" reading_time_minutes: 2 breadcrumb: [Trigger flows, MetricBase, Manage instance data sources, Extend ServiceNow AI Platform capabilities] --- # Create a MetricBase model trigger Model triggers execute when the time-series data deviates from expected values. ## Before you begin Role required: admin ## About this task There are four model types supported: autoregressive integrated moving average \(ARIMA\), probabilistic exponentially weighted moving average \(PEWMA\), Holt-Winters, and seasonal trend decomposition using Loess \(STL\). MetricBase uses the model to create a definition of expected, normal values. ## Procedure 1. Navigate to **All** > **MetricBase** > **MetricBase Triggers** > **Trigger Definitions**. 2. Select **New**. 3. On the **MetricBase** Trigger Creation form, select the **Model Trigger** option. 4. On the form, fill in the fields. The trigger levels are not initially visible on this form. You configure the levels later.
| Field | Description |
|---|---|
| Name | Name the model trigger. |
| Model | Trained model this trigger uses. See [Create and train a predictive model](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/servicenow-platform/metricbase/train-a-model.md). |
| Metric | Table metric that you want to monitor. This value is set automatically based on the model, which means you cannot set it yourself. |
| Table name | Table in the MetricBase database that contains the metric that you want to monitor. This value is set automatically based on the model, which means you cannot set it yourself. |
| Active | Option to activate the trigger. |
| Description | Description of the trigger. |
| Spike Direction | Specify whether you want to trigger only on anomalies that occur above predicted values \(upward\), below predicted values \(downward\), or both \(either\). |