#Caret r package series#

Having typed this up I've realised that I'm just describing nested CV for time series. The final model is trained, and hyperparameter values selected, based on steps (1) and (2) using the most recent data only.report performance based on whatever hyperparameter values were selected in (1), even though these may change from fold to fold.train on the entire training fold using the hyperparam values selected in (1) bionic (18.04LTS) (gnu-r): GNU R package for classification and regression training universe 6.0-78+dfsg1-1: amd64 arm64 armhf i386.split each training fold into a training subset and validation subset - using the validation subset to pick hyperparam values within each training fold.I'm wondering if the best approach for my non-stationary data would follow an approach something like: recent changes in underlying relationships are best captured when training the final model.reported performance can account for the fact that the best hyperparam values may change across splits, and.optimal hyperparameter values can change as underlying relationships change.My data are non-stationary, so I'd like hyperparameter tuning, performance reporting and final model training to be based on recent data so that: caret trains a final model using all of the data available - which makes sense when fixedWindow = TRUE but perhaps not otherwise.the 'best' hyperparameters chosen by caret are those with the best average performance across all train/test splits, and similarly the reported performance is the average across all train/test splits based on these hyperparam values, and.

#Caret r package series#

I'm using caret's timeslice method to do step-ahead cross validation on time series data.