Compute the total domestic overnight trips for holidays across Australia from the tourism dataset. a. Plot the data and describe the main features of the series.
b. Decompose the series using STL and obtain the seasonally adjusted data.
c. Forecast the next two years of the series using an additive damped trend method applied to the seasonally adjusted data. (This can be specified using decomposition_model().)
d. Forecast the next two years of the series using an appropriate model for Holt's linear method applied to the seasonally adjusted data (as before without damped trend).
e. Now use ETS() to choose a seasonal model for the data.
f. Compare the RMSE of the ETS model with the RMSE of the models you obtained using STL decompositions. Which gives the better in-sample fits? & Compare the forecasts from the three approaches? Which seems most reasonable?