Now, we can analyze seasonality—that is, how data changes across months. From our observations, we know that, for some months, sales tend to be higher, whereas for other months, sales tend to be lower. We evaluate the differences between the linear trend and actual sales. Based on the pattern observed in these differences, we produce a model of seasonality to predict sales more accurately for each month:
| Sales for January | |||||||||
| Year | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Average |
| Actual sales | 10.5 | 11.9 | 13.2 | 14.6 | 15.1 | 16.5 | 18.9 | 20 | |
| Sales on the trend line | 13.012 | 14.291 | 15.57 | 16.849 | 18.128 | 19.407 | 20.686 | 21.965 | |
| Difference | -2.512 | -2.391 | -2.37 | -2.249 | -3.028 | -2.907 | -1.786 | -1.965 | -2.401 |
| Sales for ... |
