We have data of sales in thousands of USD for a small electronics shop by month for the years 2010 to 2017. We would like to predict sales for each month of 2018:
| Month/Year | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 |
| January | 10.5 | 11.9 | 13.2 | 14.6 | 15.1 | 16.5 | 18.9 | 20 | 20.843 |
| February | 11.9 | 12.6 | 14.4 | 15.4 | 17.4 | 17.9 | 19.5 | 20.8 | 21.993 |
| March | 13.4 | 13.5 | 16.1 | 16.2 | 17.2 | 19.6 | 19.8 | 22.1 | 22.993 |
| April | 12.7 | 13.6 | 14.9 | 17.8 | 17.8 | 20.2 | 19.7 | 20.9 | 22.956 |
| May | 13.9 | 14.6 | 15.7 | 17.8 | 18.6 | 19.1 | 20.8 | 21.5 | 23.505 |
| June | 14 | 14.4 | 15.3 | 16.1 | 18.9 | 19.7 | 21.1 | 22.1 | 23.456 |
| July | 13.5 | 15.7 | 16.8 | 17.4 | 18.3 | 19.7 | 21 | 22.6 | 23.881 |
| August | 14.5 | 14 | 15.7 | 17 | 17.9 | 20.5 | 21 | 22.7 | 23.668 |
| September | 14.3 | 15.5 | 16.8 | 17.2 | 19.2 | 20.3 | 20.6 | 21.9 | 23.981 |
| October | 14.9 | 15.8 | 16.3 | 17.9 | 18.8 | 20.3 | 21.4 | 22.9 | 24.293 |
| November | 16.9 | 16.5 | 18.7 | 20.5 | 20.4 | 22.4 | 23.7 | 24 | 26.143 |
| December | 17.4 | 20.1 | 19.7 | 22.5 | 23 | 23.8 | 24.6 | 26.6 | 27.968 |
Analysis:
To be able to analyze this, we will first graph the data so that we can notice patterns and analyze them.
From the graph and the table, we notice that, in the long term, the sales increase linearly. This is the trend. However, we can also see that the sales for December tend to be higher than for the other months. Thus, we have reason to believe that sales are also influenced by the month.
How could we predict the monthly sales for the following years? First, we determine the exact long-term trend of the data. Then, we would like to analyze the change across the months.