Exploring time series forecasting with forecast()

The most logical next step after understanding a time series' features and trends is trying to forecast its future development.

As one would imagine, R provides optimal tools to perform this task.

In this recipe, we will leverage the extremely popular forecast package by Professor Rob J Hyndman. The package provides an always increasing number of tools for performing univariate time series forecasting.

You can find out more on the package on Prof. Hyndman's personal site at http://robjhyndman.com/software/forecast/.

Getting ready

As stated earlier, the only package needed to perform this recipe is the forecast package. We therefore need to install it and load it:

install.packages("forecast")
library(forecast)

How to do it...

1. Apply the stl() function to the nottem dataset:

   nottem_decomposition <- stl(nottem, s.window = "periodic")
   

2. Forecast five more years:

   forecast <- forecast(nottem_decomposition,h = 5)
   

3. Plot the forecasted values:

   plot(forecast...