Seasonality: Predicting Seasonality (from History)

This is part 3 of our series on Seasonality, previous segments are available in our archives.

Once you understand the seasonality of your data, it can be a useful tool in predicting the future. Taking into account changes that happen at different times of year will make your predictions more accurate than if you just extrapolate from the data itself.

For example, if we return to our highly seasonable same data below:

raw

If we were try to predict the future using a simple linear regression, it would fail to capture those regular, seasonal spikes. Below is a 6-degree polynomial regression on the above data:

trendline

As you can see, this regression would be very effective at predicting the value for almost any day of the year except for our big spikes around Christmas! This means that you need to use different approaches to take seasonality into account.

From what we covered yesterday, I will assume you have identified when you experience seasonality. On those dates, you need to calculate the average deviation from the regression which will become your expected change in the future. For our data above the deviations for the past few years break down as:

December 25, 2012 20.7%
December 25, 2013 20.8%
December 25, 2014 21.6%
December 25, 2015 22.0%
Mean Deviation 21.3%

 

We can then use this factor (+21.3% or 1.213) to predict the value for any upcoming Christmas by using a simple regression and multiplying the value for Christmas by our deviation factor. If our regression prediction for Christmas this year is 100, then we would estimate 100 * 1.213 = 121.3.

This works well when you have a few years of history to use, but that won’t be true of new products and services! We’ll cover what to do in those cases tomorrow.

 

Quote of the Day: In – five hundred twenty-five thousand / Six hundred minutes / How do you measure / A year in the life” Jonathan Larson, RENT (the musical) – Seasons Of Love