A common practice in Machine/statistical Learning is to split up the dataset into a test and training set. However, 2 of my textbooks on time series analysis never do this, they apply the TS algorithms on the entire dataset to predict the next datapoint in the series.
Does anyone know why this is the case? I.e, the reason for the difference in approaches?
When you split the data and do model identification the resulting model may depend on the point of the split. If you then measure performance two things are in play the form/parameters of the model and future values. Partitioning data into "training" and "non-training" requires the assumption of a fixed model with a set of fixed coefficients/parameters.
Modern time series analysis ( probably more modern than your textbook as textbooks are out of date before they are printed !) would suggest forming a model based upon the total # of data points AND then testing for parameter divergence/change possibly suggesting then that the most recent homogeneous data be used to form a useful model.
