I am analyzing time series data on electricity prices using a variety of methods (ARIMAX, TBATS, STLM) including the auto-regressive feed-forward neural network NNETAR, which is implemented in the forecast package.
Since auto.arima, tbats etc. choose parameters automatically, I've let nnetar choose the parameters automatically respectively I used the default values as well.
I only have a training set and a test set, but I have read, that it can be beneficial to further split the training set into a smaller training set and a validation set. The model is then tested on the validation set. After the optimal parameters have been obtained, a model is fitted on the "whole" training set (small training set + validation set) and tested on the test set.
Is there an implementation for auto-regressive time series parameter tuning in R (many times parameter tuning involves the caret package)?