Image: *Forecasting in [Domo.AI](http://Domo.AI) Playground via the Forecasting Universal Model*
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### Enable the Forecasting Universal Model
Contact your Domo account team to enable the Forecasting Universal Model in your instance. See [Required Grants](#required_grants) for information on controlling access to the tool when enabled.
### Required Grants
To access the Forecasting Universal Model, one of the following three grants must be enabled for your role:
* **Create AI Service Models —** Allows users to create new models and edit, deploy, and delete models you can access.
* **Use AI Services —** Allows users to access AI services for use in Domo apps, Domo Bricks, Jupyter Workspaces, and Workflows. This grant should be given to any user who needs to use AI services but does not need to manage them.
* (Admin only) **Manage AI Services —** Allows admins to create, edit, deploy, and delete all models in AI Models.
If you are using the Forecasting Universal Model via App Studio, you also need the following grant. Domo admins have this grant by default.
* **Edit App (App Studio)** **—** Create, edit, share, and delete apps in App Studio.
Learn more about [grants](/s/article/360043438973).
### Forecast in a Domo App
The Forecasting Universal Model allows you to easily add a forecast to a line or bar chart card in a Domo app. You can use App Studio to input the seasonality of your data and the number of time points you would like to forecast. The forecast is automatically added to your card along with two confidence interval ranges.
Note that the line or bar chart you add to a forecast must have the following:
* A *date* column on the x-axis (values must be stored as a timestamp data type or with a daily, weekly, monthly, quarterly, or yearly granularity level)
* A *numeric* column on the y-axis (values in this column must be stored as an integer, floating decimal, or fixed decimal data type)
* No *series* column.
In the example image below, a line chart is embedded in an app that could be used for forecasting using the Forecasting Universal Model. The "Number of Orders | By Month" card uses a date column on the x-axis and a numeric column on the y-axis.
3. Find and select the specific card you want to add the forecast to.
The card displays with a blue outline and the right configuration panel opens.
4. In the **Settings** tab of the right configuration panel, select **Add Forecasting with AI**. If this option is not enabled, then the card is not compatible with the Forecasting Universal Model.
5. After configuring your settings, save them and close the app.
6. Open the app in view mode.
7. Locate the card and select **Generate AI Forecast**.
After a few seconds, the forecast should appear on the card.
You can interpret the forecast as follows:
* **Value (blue line) —** Data points from the numeric column displayed on the Y-axis; the forecast is based on patterns or trends in this data.
* **Forecast (gray line) and forecastValue —** Predicted data points by the Forecasting Universal Model; these points are equal to the average of the most probable potential results.
* **Range (darker blue shading) —** A narrower range of probable potential results; we are 80% confident that the actual data points will fall within this range.
* **Range2 (lighter blue shading) —** A wider range of probable potential results; we are 95% confident that the actual data points will fall within this range.
* **Conf0.8Upper —** Data point that corresponds to the upper bound (such as the largest value) of the range.
* **Conf0.8Lower —** Data point that corresponds to the lower bound (such as the smallest value) of the range.
* **Conf0.9Upper —** Data point that corresponds to the upper bound (such as the largest value) of the Range2.
* **Conf0.9Lower —** Data point that corresponds to the lower bound (such as the smallest value) of the Range2.
3. Makes edits to the
[settings](#table_forecast_settings).
After saving your changes, the updated forecast displays on the card.
# Remove the Forecast
To remove a forecast that has been added to a card embedded in a Domo app, follow these steps:
1. [Open the app](#generated_step_onetwo) in App Studio edit mode and select the card containing the forecast to open the right configuration panel.
2. In the **Settings** tab of the configuration panel, select **Configure AI Forecast**. The **AI Forecasting** modal displays.
3. In the modal, select **Remove**.
After saving your changes, the option to **Generate AI Forecast** is removed from the card.
### Use the Forecasting Service in Domo. AI Playground
You can try out various configurations of the Forecasting Universal Model and compare the resulting outputs via the Forecasting Service in Domo. AI Playground. From the Forecasting Service, you can also extract the cURL code that generates the forecast and use the code in other settings, like interacting with an API.
In the left sidebar, select 
# Use the Forecasting Service
To test various configurations of the Forecasting Universal Model via the Forecasting Service and compare to the resulting forecasting output, you must have the [required grants](#required_grants) enabled and follow these steps:
1. Configure the forecasting settings in the **Configuration** pane. The table below describes the configuration settings.
3. (Optional) Expand the
**Output** pane by selecting 
# Interpret Forecast Results
The forecast results can be interpreted as follows (see highlighted text). You receive this information for each time point of your forecast in the **O** **utput** pane.
For example, if you select Forecast Length = 8 in the **Configuration** pane, you receive the forecast results for each of the eight time points forecasted.
If you expand the **Code Snippet** pane, you can view the block of cURL code that generates the forecast output. You can select **Copy Code** to copy this code to your machine's clipboard and use it in other settings, such as interacting with an API.
**Note:** To regenerate the forecast based on different configurations, change the settings in the **Configuration** pane and select **Run**.
### Change the Default Forecasting Universal Model
The default Forecasting Universal Model is an ARIMA (AutoRegressive Integrated Moving Average) model. An ARIMA model is a forecasting method that combines autoregression and differencing to make the data stationary and a moving average of past forecast errors. Data scientists commonly use ARIMA models for forecasting.
The [AI Service Layer Settings](https://domo-support.domo.com/s/article/000005279?language=en_US) allow you to change the default Forecasting Universal Model to a model you built or from an external third party.
### FAQ
Date | Revenue |
|---|---|
2023-03 | 134,589.12 |
2023-04 | 144,567.45 |
2023-05 | NULL |
2023-06 | 154,387.98 |
Date | Revenue |
|---|---|
2023-03 | 134,589.12 |
2023-04 | 144,567.45 |
2023-06 | 154,387.98 |