Dear Nixtla-Team, is it somehow possible to run a rolling prediction just like cross validation does without refitting the model? For example I want to load a model from file and evaluate its performance on some set of new time series each containing far more data points that the horizon of the model. I am already considering hacking up something myself from the core.py predict and crossvalidation methods.

You can use the

cross_validation

method. You will first need to change the number of

max_steps

to 0, so when it calls the

fit

method it doesn't run any iteration. You can do this with the following function:

def set_trainer_kwargs(nf, max_steps, early_stop_patience_steps):
	## Trainer arguments ##
	# Max steps, validation steps and check_val_every_n_epoch
	trainer_kwargs = {**{'max_steps': max_steps}}

	if 'max_epochs' in trainer_kwargs.keys():
		raise Exception('max_epochs is deprecated, use max_steps instead.')

	# Callbacks
	if trainer_kwargs.get('callbacks', None) is None:
		callbacks = [TQDMProgressBar()]
		# Early stopping
		if early_stop_patience_steps > 0:
			callbacks += [EarlyStopping(monitor='ptl/val_loss',
										patience=early_stop_patience_steps)]

		trainer_kwargs['callbacks'] = callbacks

	# Add GPU accelerator if available
	if trainer_kwargs.get('accelerator', None) is None:
		if torch.cuda.is_available():
			trainer_kwargs['accelerator'] = "gpu"
	if trainer_kwargs.get('devices', None) is None:
		if torch.cuda.is_available():
			trainer_kwargs['devices'] = -1

	# Avoid saturating local memory, disabled fit model checkpoints
	if trainer_kwargs.get('enable_checkpointing', None) is None:
		trainer_kwargs['enable_checkpointing'] = False

	nf.models[0].trainer_kwargs = trainer_kwargs
	nf.models_init[0].trainer_kwargs = trainer_kwargs

Works great, thank you!