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A **naive forecast** is one in which the forecast for a given period is simply equal to the value observed in the previous period.

For example, suppose we have the following sales of a given product during the first three months of the year:

The forecast for sales in April would simply be equal to the actual sales from the previous month of March:

Although this method is simple, it tends to work surprisingly well in practice.

This tutorial provides a step-by-step example of how to perform naive forecasting in Excel.

**Step 1: Enter the Data**

First, we’ll enter the sales data for a 12-month period at some imaginary company:

**Step 2: Create the Forecasts**

Next, we’ll use the following formulas to create naive forecasts for each month:

**Step 3: Measure the Accuracy of the Forecasts**

Lastly, we need to measure the accuracy of the forecasts. Two common metrics used to measure accuracy include:

- Mean absolute percentage error
- Mean Absolute Deviation

The following image shows how to calculate mean absolute percentage error:

The mean absolute percentage error turns out to be **9.9%**.

And the following image shows how to calculate mean absolute deviation:

The mean absolute deviation turns out to be **3.45**.

To know if this forecast is useful, we can compare it to other forecasting models and see if the accuracy measurements are better or worse.