> For the complete documentation index, see [llms.txt](https://docs.fabricplan.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fabricplan.com/user-guide/6-forecasts-rolling-forecasts-and-statistical-forecasts/6-4-statistical-forecasts.md).

# 6.4 - Statistical forecasts

Learn how to create statistical forecasts using historical trends, seasonality patterns, confidence ranges, growth factors, and evaluation methods in a planning sheet. Statistical forecasts use historical data to generate projected values and provide forecast previews that can be reviewed, exported, and saved to forecast measures.

## Configure a statistical forecast

1. Create a forecast measure with blank data input columns.

   ![](/files/M8SuKvXyhoG1VIPYjBqH)

   <div data-gb-custom-block data-tag="hint" data-style="info" class="hint hint-info"><p>Statistical forecasts require at least two years of historical data.</p></div>
2. Select the cell whose value must be predicted.

   ![](/files/xFnUITG5H1WePqjx5Dyh)
3. Select **Predict** in the **Model** ribbon.

   ![](/files/AbzR2WcvLOCFyiThKXjn)
4. Select the lock icon to freeze the row and measure selection.

   ![](/files/bcgipw7vShuX7fKrBh73)
5. Select **Profile 1** from the profile list.

   ![](/files/sQKhArq7RlRafLwxMaPH)
6. Select the profile options button to create, rename, or delete a forecast profile.

   ![](/files/lq0xjutRryKfK3CJV5eh)
7. Select **Jan 2024 - Dec 2025** as the historical date range used for forecast calculations.

   ![](/files/LaLzCqakRS06HVbYSKvR)
8. Select **Jan 2026 - Dec 2026** as the forecast date range. By default, the open period is selected. Extend the forecast beyond the open period if required.

   ![](/files/CfIzQGvniVRkJ97vUxGI)
9. Select **Multi-Seasonality** if the data contains repeating patterns across multiple seasonal levels, such as quarterly and monthly patterns.

   ![](/files/kDzlY4pPx2SScstQqvpN)
10. Select **Single Seasonality** to identify one repeating seasonal pattern.

    ![](/files/s2TxQSvVb6qfkbcFduSx)
11. Set **Confidence (%)** to **90** to define the forecast confidence range.

    ![](/files/H4H8ZsxNoWUcV7sWd4F1)
12. Configure the **Growth Factor (%)** to increase or decrease the projected forecast growth rate.

    ![](/files/qXXC2Gqa2fNSzqssfAxx)
13. Select **Bottom Up** to calculate forecast values for child nodes and aggregate the results to totals and subtotals.

    ![](/files/Wfx5t9mrD3FS7LfRu2jo)
14. Select **Top Down** to calculate forecast values for totals and subtotals, and then distribute the results to child nodes.

    ![](/files/yfyMrxYs9oUojIw5Qzbe)
15. Select **Run Forecast**.

    ![](/files/fLahrYurU6BcWKY6BbVG)
16. The Forecast Preview section displays predicted values. Historical data appears in gray, predicted values appear in green, and the confidence range appears as green shading in the background.

    ![](/files/cTCqlybQKMBUuUIvOzOq)
17. Select **Table** to view forecast and historical data values in tabular format.

    ![](/files/x9eLKrfME50EQzK20Djo)
18. Select the export arrow next to **Save Forecast** to either export your values to CSV, or to save your forecast data to an existing scenario.

    ![](/files/Px4TqsBcxt9veVuDyZ7X)
19. Select **Save Forecast** after configuring the forecast parameters.

    ![](/files/kaznFhAwbfJGqLxlOyiX)
20. Select **Save** to save forecast values to the forecast measure.

    ![](/files/ylDEKdrQ3PhisD7MnqYN)
21. The statistical forecast is created for the selected category and period. In this example, a bottom-up forecast was created, with values calculated and aggregated across row and column hierarchies.

    ![](/files/1R6x6F5wsPUZsCpb8FsL)


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