Forecasting time series

After using HoltWinters to build a time series smoothing model, we can now forecast future values based on the smoothing model. In this recipe, we introduce how to use the forecast function to make a prediction on time series data.

Getting ready

In this recipe, you have to have completed the previous recipe by generating a smoothing model with HoltWinters and have it stored in a variable, m.pre.

How to do it…

Please perform the following steps to forecast Taiwan Semiconductor's future income:

1. Load the forecast package:

   > library(forecast)
   

2. We can use the forecast function to predict the income of the next four quarters:

   > income.pre <- forecast.HoltWinters(m.pre, h=4)
   > summary(income.pre)
   
   Forecast method: HoltWinters
   
   Model Information:
   Holt-Winters exponential smoothing with trend and additive seasonal component.
   
   Call:
   HoltWinters(x = m)
   
   Smoothing parameters:
    alpha: 0.8223689
    beta : 0.06468208
    gamma: 1
   
   Coefficients:
            [,1]
   a  1964.30088
   b   ...