> For the complete documentation index, see [llms.txt](https://help.modelreef.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.modelreef.io/how-tos/scenarios-and-planning/build-a-forecast-accuracy-tracker.md).

# Build a Forecast Accuracy Tracker

This guide explains how to build a forecast accuracy tracker using multiple Model Reef models over time and actuals imported from your accounting system or historical data sources.

Forecast accuracy is not a single model feature. It is an analytical process that compares what the model predicted in the past with what actually happened.

***

## Before you start

You should have:

* A process of saving periodic snapshots of your model, for example monthly or quarterly.
* An Actuals model that reflects realised financial performance.
* The ability to export key metrics from your models.

If you do not yet snapshot models, establish a simple naming convention first, such as `Company - Forecast - Jan`, `Company - Forecast - Feb` and so on.

***

## What you will build

* A time series of forecasts for selected metrics from prior model versions.
* A set of realised actual values for the same metrics.
* A view of forecast error over time and by horizon.

***

{% stepper %}
{% step %}

### Define which metrics and horizons to track

Decide which metrics you care about, for example:

* Revenue
* Gross profit
* EBITDA
* Cash balance
* Capex

For each metric, decide which forecast horizons matter, for example:

* One month ahead
* Three months ahead
* Twelve months ahead

Clarity on metrics and horizons keeps the tracker focused.
{% endstep %}

{% step %}

### Snapshot your forecast models periodically

On a regular cadence (monthly or quarterly):

* Save a copy of your current forecast model with a date specific name.
* Do not modify historical snapshots once created. They represent your state of knowledge at that time.
* Continue this over time so you accumulate a sequence of forecast models.

Each snapshot contains what the model predicted at that point.
{% endstep %}

{% step %}

### Maintain an Actuals model

Maintain a separate Actuals model that:

* Imports actual results from your accounting system or historical sources.
* Covers the same periods as the forecasts you want to evaluate.

Keep this model up to date as new actuals become available.
{% endstep %}

{% step %}

### Extract forecast and actual values

For each snapshot model and the Actuals model:

* Export selected metrics for relevant periods.
* Store them in an external table or analysis file with columns such as:
  * Snapshot date
  * Target period date
  * Forecast value
  * Actual value

This creates the raw data for accuracy analysis.
{% endstep %}

{% step %}

### Calculate forecast error

In your analysis file, calculate error measures, for example:

* Absolute error: Forecast minus Actual
* Percentage error: (Forecast minus Actual) divided by Actual
* Absolute percentage error where sign is not important

You can then summarise these by:

* Horizon (for example average twelve month ahead error)
* Metric
* Snapshot date (to see if forecasting is improving)
  {% endstep %}

{% step %}

### Interpret and act on accuracy insights

Use the forecast accuracy tracker to:

* Identify systematic bias, for example:
  * Persistent over estimation of revenue
  * Underestimation of costs
* Improve driver design and assumptions in future models
* Communicate uncertainty ranges rather than precise point forecasts where appropriate

Model Reef provides the modelling engine. The accuracy tracker is a layer on top that improves how you use that engine over time.
{% endstep %}
{% endstepper %}

***

## Check your work

* Forecast snapshots are clearly labelled and not edited retrospectively.
* Actuals are mapped consistently with the metrics being tracked.
* Error measures are computed reliably and interpreted with care.
* Insights from accuracy analysis feed back into assumption and model design.

***

## Troubleshooting

<details>

<summary><strong>Difficult to align forecast and actual periods</strong></summary>

Ensure that model calendars and accounting periods align, or adjust exported data to a common period framework before comparison.

</details>

<details>

<summary><strong>Large percentage errors in low volume periods</strong></summary>

Focus on periods and metrics where the absolute level is meaningful or use appropriate thresholds.

</details>

<details>

<summary><strong>Accuracy appears to worsen after major changes</strong></summary>

This can indicate that new product lines or regimes need separate modelling approaches. Adjust your forecasting design, not just your expectations.

</details>

***

## Related guides

* [Grant Funding Models](/use-cases/not-for-profit-and-education/grant-funding-models.md)
* [Branch Hierarchy Rules](/help/building-your-model/branch-hierarchy-rules.md)
* [Branch Level Permissions](/help/permissions-and-collaboration/branch-level-permissions.md)
* [Driver Editor Screen](/syntax/understanding-the-interface/driver-editor-screen.md)