I would like to perform time series cross validation on a fairly large dataset.
To see if the results are consistent over multiple folds.
The way I approach this is to stop training at certain points in time, then predict the next 2 weeks of my data. After this I can continue training (see figure below.)
I would like to perform time series cross validation on a fairly large dataset. To see if the results are consistent over multiple folds.
The way I approach this is to stop training at certain points in time, then predict the next 2 weeks of my data. After this I can continue training (see figure below.)
Looking at the source code pts/model/estimator.py I can see a function train_model : https://github.com/zalandoresearch/pytorch-ts/blob/5da3be5abd4a81de7a81949fe2cf24fde44cb171/pts/model/estimator.py#L89
Which outputs a trained neural network
The other function I use now train: https://github.com/zalandoresearch/pytorch-ts/blob/5da3be5abd4a81de7a81949fe2cf24fde44cb171/pts/model/estimator.py#L164
Creates a predictor object
I'm not sure how to combine these functions to achieve the desired result, anyone has experience in using this?