Given a set of values as

``````10
12
14
16
18
``````

I would like to "estimate" that the next values will be

``````20
22
24
``````

and so on. I realize there are several different ways to extrapolate, and they will give different results. I'm assuming though that the number of options will be rather limited, so I'm not to picky about which method is used.

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There are two ways I can think of:

Regression

With the following function, one can retrieve the linear regression coefficients of two sets of data:

``````LINEST(y-axis,x-axis) ==> y=ax+b
``````

The second set of data is the accompanying x-axis. This can be a normal numbering, as shown in the first sheet of the example file.

Once the regression factors are known, one can extrapolate any unknown x or y beyond the known set of data:

• known x: ax+b
• known y: (-b+y)/x

Since R2=1, the numbers given are exact values. I formated the values to be rounded in the example, because the calculation shows floating point errors.

Dragging

Google has a way of predicting the outcome of a serie, based on that serie. If you drag the serie like shown in the screenshot, then the outcome will appear automatically:

Example

I've created an example file for you: Extrapolate
A more practical example: http://webapps.stackexchange.com/a/35668/29140

Remark

The regression method will give you always a value, based on the factors. The more un-linear the sets of data are (R2<1), the more predicted they are.

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Thank you, I'll give it a try. I'm aware of the "dragging" example, and I realize from my simple example that could seem as a viable solution, but I'm working with more complex data in my real sheet. :) – user50849 Aug 26 '13 at 20:26
@user50849 See the second reference, under example, for a more mixed range. Was my answer helpfull? – Jacob Jan Tuinstra Sep 7 '13 at 10:00
It was indeed, and it solved my problem. Accepted the answer :) – user50849 Sep 7 '13 at 10:18

There is a `forecast` function built in to Google Spreadsheets that makes this easy to do for simple cases using a linear regression. The docs for forecast are good and clear.

The basic use case is:

``````FORECAST(x_value_to_forecast,y_axis_set,x_axis_set)
``````

This function combines the steps of calculating the regression coefficients and applying them to the datasets.

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