With this task, the objective is to produce a univariate forecast for the surface temperature, focusing on choosing either a Holt linear trend model or an ARIMA model. We will train the models and determine their predictive accuracy on an out-of-time test set, just like we've done in other learning endeavors. The following code creates the temperature subset and then the train and test sets, starting after WWII:
> temp <- climate[, 2] > temp <- climate[, 2] > train <- window(temp, start = 1946, end = 2003) > test <- window(temp, start = 2004)
To build our smoothing model, we will use the holt() function found in the forecast package. We will build two models, one with and one without a damped trend. In ...
