Doing the forecast
To calculate the forecast of a time series, we have to multiply the seasonal irregularity by the trending data of the regression model. With this, we will have the ups and downs of the seasonal irregularity in our forecast. These results are explained in the following figure:
Figure 3.7 – Forecast
The preceding figure shows the components of the forecast of our time series. We have the seasonal or cycling data with the up-trending line to do a forecast of the multiplication of the seasonal irregularity trend that we discussed before.
Like the regression model, the time series forecast just gives us an idea of what could happen in the future; it is not an exact prediction. For example, we can see that for the fourth semester of year 11, we will see a growth in passenger demand. This makes sense with the past data showing an increased passenger demand in the fourth trimesters of almost all the past years. The relatively low increment...
