Olivier Sprangers

> Thanks @elephaint do you have any idea when this will available in a release ? Not yet

~To do what you want, you can access the `default_config ` attribute of the Auto* models, and make changes:~ [deleted] Update: the suggested solution doesn't work. Sorry for the inconvenience.

Do you have an example of such an automated pipeline? The linked PR doesn't talk about Auto* models and I don't see an example piece of code of an automated...

Thanks - Weighing the pros and cons for now we will update the docs to demonstrate the ability to access the attribute to attain the default config. For the sktime...

> updating the default config didn't worked for me I tried doing same mentioned above by @elephaint, accessing the dictionary keys and updating the values for `AutoLSTM`. Opening an issue...

@steffenrunge I think you can use cross-validation to perform what you want. Cross-validation will at the end refit the model with the entire training data based on the best hyperparams....

@WenjuanOlympus I think you are right that in our implementation we only use the future_covariates for the input_size. However, I don't think this is necessarily problematic.

Hi, thanks for using nixtla! I need a bit more information - can you please share a full, standalone piece of code that reproduces the issue? (the above code is...

> thank you so much @elephaint, it seems it involved a lot of effort. i'm very sorry about asking a change to his pr, but would you be open to...

Wrt missing values tutorial: - You can only manually put the doc in the right subcategory in readme.com (by using the Admin panel). Yes, that't not ideal.... I put the...