There are some validation test statistics whose proofs require the train and test sample to be separated by a small burn-in sample to avoid dependence between the two samples (mostly to address residual dependence when the model is not correctly specified). For instance Proposition 3 Chapter 4 in Andree, B. P. J. (2020). Theory and Application of Dynamic Spatial Time Series Models. Rozenberg Publishers and the Tinbergen Institute, propose a Diebold Mariano statistic that tests the significance of Log Likelihood differences on a validation sample with a small burn-in.
Below is a simple modification of the time slices function that would make such things easier to execute.
Thanks for making one of the best R packages ever!
I'd like to suggest a minor feature for the function
createTimeSlices
inside https://github.com/topepo/caret/blob/master/pkg/caret/R/createDataPartition.RThere are some validation test statistics whose proofs require the train and test sample to be separated by a small burn-in sample to avoid dependence between the two samples (mostly to address residual dependence when the model is not correctly specified). For instance Proposition 3 Chapter 4 in Andree, B. P. J. (2020). Theory and Application of Dynamic Spatial Time Series Models. Rozenberg Publishers and the Tinbergen Institute, propose a Diebold Mariano statistic that tests the significance of Log Likelihood differences on a validation sample with a small burn-in.
Below is a simple modification of the time slices function that would make such things easier to execute.
Here I'm using it with a single observation as burn-in:
I added a simple error message when the burn-in sample leads to discarding the entire validation sample:
Kind regards, Bo