# Build a Rolling Forecast

This guide explains how to build a rolling forecast in Model Reef by combining imported actuals with forward-looking drivers, and updating the forecast window over time.

{% hint style="info" %}
Before you start

You should have:

* A model with imports set up for actuals (for example from Xero or QuickBooks) or historical data imported via PDF or Excel.
* Forward-looking drivers and variables that generate forecasts beyond the historical period.
* A clear idea of how often you want to roll the forecast forward, for example monthly or quarterly.

If you have not set up imports yet, see:

* **PDF Import Overview**
* **Xero Integration**
* **QuickBooks Integration**
  {% endhint %}

***

## What you will build

* A model where:
  * Historical periods are populated with actuals.
  * Future periods are driven by assumptions.
* A process for periodically:
  * Refreshing actuals.
  * Extending the forecast horizon.
  * Reviewing updated results.

***

{% stepper %}
{% step %}

### Set up the historical data import

In your model:

* Connect to Xero or QuickBooks, or import historicals via PDF or Excel.
* Map accounts or lines to appropriate types and categories.
* Confirm that:
  * P\&L, Balance Sheet and Cashflow show historical results correctly.
  * The model start date and frequency match your intended forecast structure.

Historical periods should be clearly distinguished from forecast periods in your own documentation or charts.
{% endstep %}

{% step %}

### Build forward looking drivers and variables

Ensure that for each major line item, you have forward projection logic, for example:

* Revenue:
  * Unit growth and price drivers.
* COGS:
  * Cost per unit drivers.
* Opex and Staff:
  * Drivers for headcount, spend ratios or fixed amounts.
* Capex:
  * Planned investment schedules.
* Financing:
  * Debt and equity events.

These drivers should operate from the forecast start date onward.
{% endstep %}

{% step %}

### Blend historical and forecast periods

Model Reef keeps historical and forecast series conceptually separate but produces a combined time series:

* Historical values occupy periods up to a chosen cut off.
* From that point on, model logic generates values.

Make sure that:

* The transition point between historical and forecast periods is sensible.
* There are no artificial jumps at the boundary, unless they reflect real changes.

You can use charts to see where the historical segment ends and the forecast begins.
{% endstep %}

{% step %}

### Define your rolling update cadence

Decide how often you will:

* Refresh historicals.
* Extend the forecast horizon.

Typical cadences:

* Monthly rolling forecast:
  * Update actuals each month as new data arrives.
  * Maintain a fixed forecast length, for example the next twelve or twenty-four months.
* Quarterly rolling forecast:
  * Update every quarter.
  * Maintain a longer horizon, for example three to five years.

Document this cadence so your team understands when the numbers are expected to move.
{% endstep %}

{% step %}

### Perform a rolling update

When a new period of actuals is available:

* Refresh the import from your accounting system or historical data source.
* Confirm that:
  * Historical periods now include the latest actual data.
  * Forecast logic now starts from a later period.
* Extend forecast drivers if needed so that the horizon stays the same overall length.

Review changes in:

* P\&L.
* Cash Waterfall.
* Valuation outputs.

This rolling process is repeated on your chosen cadence.
{% endstep %}

{% step %}

### Track changes over time

To understand how the forecast evolves:

* Periodically save copies of the model, for example:
  * `Company - Rolling Forecast - Jan`
  * `Company - Rolling Forecast - Feb`
  * `Company - Rolling Forecast - Mar`
* Compare:
  * Forecast revenue and margin changes.
  * Updates to cash and funding needs.
  * Valuation movements.

You can also export key metrics after each update to maintain a longitudinal view outside Model Reef.
{% endstep %}
{% endstepper %}

***

{% hint style="info" %}
Check your work

* Historical periods are correctly populated and reconciled.
* Forecast logic picks up at the correct point and remains meaningful as time passes.
* Rolling updates do not break the model or create large unexplained jumps.
* Stakeholders understand that the forecast is a living document, not a static plan.
  {% endhint %}

***

## Troubleshooting

<details>

<summary>Forecast stops too early after rolling</summary>

Extend your drivers and variables further into the future so that the horizon remains constant as you roll forward.

</details>

<details>

<summary>Actuals overwrite expected behaviour in future periods</summary>

Check that your import and mapping only apply to past periods and that forecast logic is set up for new ones.

</details>

<details>

<summary>Difficult to track how forecasts have evolved</summary>

Establish a simple naming convention and snapshot process, or maintain a separate log of key metrics over time.

</details>

***

## Related guides

* [Fuel & Maintenance Forecasting](/use-cases/logistics-transport-and-fleet/fuel-and-maintenance-forecasting.md)
* [Archiving Models](/help/building-your-model/archiving-models.md)
* [Asset Variables: what is, rules, what it affects](/help/drivers-variables-and-timing/asset-variables.md)
* [Creating Scenarios](/syntax/scenario-syntax/creating-scenarios.md)


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