Smart Prediction Methodology

How Smart Prediction calculates your forecast values

Updated over a week ago

What is Smart Prediction?

The Fathom Smart Prediction value rule allows you to use linear regression or a rolling average to forecast your company’s future financials.

Linear regression draws a line of best fit through the data to forecast future periods while the rolling average calculates your forecast’s values based on the average of the most recent x months of data. We built this functionality into Fathom to enable you to leverage your business's historical performance, without leaning on methods, like last year's actuals, that are more fixed in nature.

The core principles of our Forecasting tool are auditability and transparency of forecast data. Which is why we have opted out of developing any black box machine learning methods that are often difficult to recalculate. Instead our Smart Prediction functionality uses mathematical formulas you can trust.

Smart Prediction is just one value generation method that can be used to calculate your forecast. To read more about all of our value rule methodologies, pleasesee the ‘Setting up Value Rules in a Fathom Forecast’ article.

Smart Prediction short term

When applying a Smart Prediction value rule, you can tell Fathom how much prior period data to use in the linear regression or average model to predict your future values. You can select from the following date ranges in the short term:

  • 3 months

  • 6 months

  • 12 months

  • 24 months

We built in some flexibility to these rules in order to allow you to select the data that you believe to be most representative of your future periods. This can be handy when looking to forecast out future periods based on your most recent results - particularly relevant during volatile economic conditions, like those seen in 2020!

Smart Prediction long term

If you choose to use 12 or 24 months of actuals data when utilizing either the linear regression or average Smart Prediction option, Fathom is able to factor seasonality into the statistical model. This allows you to account for any numerical trends and changes to the chart of account line items, that are tied to seasonal conditions.

When using the average option, you can also include a currency or percentage increase or decrease as part of the average calculation. This increase or decrease can be applied monthly, to a specific month each quarter, or to a specific month each year.

FAQ: Why are my forecast values negative?

Smart Prediction finds the line of best fit according to your historical data and continues that trended line forward into the forecast. If the historical data has a downward trend, then this will eventually result in negative values in your forecast.

You can correct for this in one of three ways:

  1. Choose a different date range of historical data (prior 3 months, 6 months, 12 months, or 24 months) to base the Smart Prediction calculation on and change the calculated trend.

  2. Choose a different value rule for the account or the time period for which the forecasted values are negative.

  3. Uncheck the 'Allow for negative values' checkbox. The downward trend will still be present in your forecast, but the values in the account will only be able to go down to 0. The image below shows how to do this:

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