I am working with the in built Air Passenger data set in R to learn forecasting.
After splitting the data in
120:24 data points, I am trying to extract trend component from them.
For the training data with
120 data points I did
trend_logtrain<- ma(log_train, order = 12, centre = T) because the data is recorded monthly so aptly the order of moving average is
12. But as it should, I have
6 missing values from the start and
6 missing values towards the end.
Similarly, to extract the trend component of test data, I did the same
trend_logtest<- ma(log_test, order = 12, centre = T) and instead of having trend for
24 data points, now I have trend values for
12 data points with the rest of them
My question is, do we proceed accordingly and only use the
12 test data points for validating a forecasting model? Or is there a way where we can extract the trend properly and still make use of all
24 data points ?
Because, reconstructing the test data like
Trend * Seasonality * Random with
12 missing values in
Trend will cause in
12 predictions instead of