---
schema_version: "1"
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title: Use Single Variate Forecasting
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version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/data/264-0-0/c360_a_ai_predictive_model_forecast_holt.md
canonical_url: https://help.salesforce.com/s/articleView?id=data.c360_a_ai_predictive_model_forecast_holt.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 6588e4afacdaeaee3be197c6b98c695df81b58b4af52de0fec3525d5e887f296
description: Predict future values based on historical patterns in a single variable by using the Holt-Winters algorithm. For example, use a data transform to forecast sales by account based on opportunity data.
products:
  - Data 360
release: "264"
---
# Use Single Variate Forecasting

Predict future values based on historical patterns in a single variable by using the Holt-Winters algorithm. For example, use a data transform to forecast sales by account based on opportunity data.

### Required Editions

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small edition"><colgroup><col></colgroup><tbody><tr class=""><td class="slds-cell-wrap" style="vertical-align:top;">Available in: <strong>All Editions</strong> supported by Data 360. See <a href="https://help.salesforce.com/s/articleView?id=data.c360_a_dc_editions.htm&amp;language=en_US" target="_blank">Data 360 edition availability</a>.</td></tr></tbody></table>

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small permission">
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            <th scope="colgroup" class="slds-cell-wrap" colspan="2" style="text-align:center;vertical-align:top;" id="d115737e59">USER PERMISSIONS
              NEEDED</th>
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        </thead>
        <tbody>
          <tr class="">
            <td data-label="USER PERMISSIONS NEEDED" class="slds-cell-wrap" style="vertical-align:top;" headers="d115737e59 ">Allow users to manage models in AI Models</td>

            <td class="slds-cell-wrap" style="vertical-align:top;" headers="d115737e59 ">Enables you to create, update, and delete models in AI Models.</td>

          </tr>
        </tbody>
      </table> <table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small permission">
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            <th scope="colgroup" class="slds-cell-wrap" colspan="2" style="text-align:center;vertical-align:top;" id="d115737e89">PERMISSION SETS </th>
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            <td data-label="PERMISSION SETS" class="slds-cell-wrap" style="vertical-align:top;" headers="d115737e89 ">Data Cloud Architect</td>

            <td class="slds-cell-wrap" style="vertical-align:top;" headers="d115737e89 ">Admin-level access to all AI Models features, including the ability to create,
              update, delete, and activate models.</td>

          </tr>
          <tr class="">
            <td data-label="PERMISSION SETS" class="slds-cell-wrap" style="vertical-align:top;" headers="d115737e89 ">Data Cloud User</td>

            <td class="slds-cell-wrap" style="vertical-align:top;" headers="d115737e89 ">Restricted access to use a model, including getting predictions and
              improvements derived from a model.</td>

          </tr>
        </tbody>
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## Run a Forecast Model with a Batch Data Transform

Use a batch data transform to apply forecasting across large datasets.

1. From Data 360, go to Data Transforms, and click go to Data Transforms, and click **New**.
2. Select **Batch Data Transform**.
3. Select **Data Model Objects** and the data space.
4. To add your input data, select a data model object. For this example, use Opportunity.
    
    ![Data Transforms where you add inputs for forecasting](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/data_cloud/images/c360_a_ai_predictive_model_forecast_inputs.png)
5. Add a node to select an AI function.
    
    ![The data transform node shows the forecast AI function.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/data_cloud/images/c360_a_ai_predictive_model_forecast_ai_function.png)
6. Select **Forecast**.
7. Select the Holt-Winters forecasting model.
8. Configure these details and input fields.
    1. Specify a forecast horizon, such as 3 (months).
    2. Keep the auto seasonality selected.
    3. Select the Sum of Total Amount as the aggregate metric to forecast.
        
        ![Configuration of input data with an aggregate metric](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/data_cloud/images/c360_a_ai_predictive_model_forecast_aggregate.png)
    4. Group data by customer account ID, and configure the close date field by year/month to ensure monthly granularity.
        
        ![Configuration to group input data.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/data_cloud/images/c360_a_ai_predictive_model_forecast_group_data.png)
    5. Specify the time series, such as the Close Date field, and select **Year-Month** to forecast out by month.
    6. Map the output field (forecasted value, upper bound, and lower bound). This action saves the transformed data to a new DMO.
    7. Save your settings. A preview shows data in three forecasted fields based on your horizon of 3.
        
        ![Data transform preview shows columns with forecast data.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/data_cloud/images/c360_a_ai_predictive_model_forecast_preview.png)
    8. Run the transform to incorporate historical data with the future forecasted rows in the output DMO.
    9. To append the transformed data back to the Account DMO, use the Join node.
        
        ![Append the transformed data and save it to a DMO.](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/data_cloud/images/c360_a_ai_predictive_model_forecast_append_data.png)
    10. Select the forecast fields to add to the Join.
    11. In the data transform, connect the Join node to Output to append forecasted values to the output object.