Magnus Jensen

> Hi @Allena101, > > I think you misunderstood what `triage` meant, it's just a way for us to classify issues and be able to filter them. `triage` just mean...

> NBeats is a deep learning model whereas LightGBM is a regression model, hence the difference is available method/approach, especially for the saving/loading of models. > > `save_checkpoints` in Darts...

> Sorry for the confusion: the code snippet you use, based on optuna, does not perform a gridsearch but use more complex algorithm to sample the parameters ([documentation](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/003_efficient_optimization_algorithms.html)) and it's...

> Hi @Allena101, > > Sorry for the delay, got busy with other things. > > By default, the trainer generates a checkpoints at the end of each epoch but...

> Hi @Allena101, > > This seems to be a purely modeling problem; the model are indeed seeing the entire dataset but optimizing their loss don't guarantee "coherence" of the...

> Hi @Allena101, > > Running your code snippet returns the expected result: the epochs_trained attributes is `15`. However, if you call `predict()` or `fit()` again, a new PyTorch-Lightning trainer...

in the notebook you linked, both examples manually create a new model and then load the weights from the previously saved model using load_weights_from_checkpoint or load_weights. So there is now...

> It's possible but it becomes with no guarantees about the model attributes "correctness" because a new `Trainer` will be created and some of the model's attributes are going to...

probably the simplest work around would be to just forecast 2 weeks and then just measure the rmse (or whatever metric you decide to use) on the last week.

another thing that is related to this (and why i asked this in the first place) is that i am looking to forecast a period in a manner that is...