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.


1

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.

2

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.

3

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.

4

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.

5

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)

6

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.


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

chevron-rightDifficult to align forecast and actual periodshashtag

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

chevron-rightLarge percentage errors in low volume periodshashtag

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

chevron-rightAccuracy appears to worsen after major changeshashtag

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


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