Javier Escobar Ortiz
Javier Escobar Ortiz
Hello @yarnabrina Thanks for opening the issue, we really appreciate it. The error seems to occur during the sickit-learn regressor.fit(), the feature names (columns) must be of the same type....
Thank you @Sergio-Quijano-Stratesys! I like the one with the raise exception as well. Please, include it as a `TypeError`.
Issue fixed in skforecast 0.12.0, thanks @Sergio-Quijano-Stratesys
Hello @KishManani Thanks for opening the issue! It is a great point to give the users more flexibility. I wonder if you will be able to do this using a...
Hello Kishan, As it is now, the ForecasterAutoregMultiVariate is only trained in one level (series 1 or series 2) and creates one model per step (direct approach). In the figure,...
Hello @sharmuz, Thanks for opening the issue. Yes, we are working on it and hope to have a final version in the next few weeks. 😄 Regarding your question, yes....
Hello @sharmuz , The functionality to include series-specific exogenous variables in ForecasterMultiSeries is now available in skforecast 0.12.0: https://skforecast.org/latest/user_guides/multi-series-with-different-length-and-different_exog Hope it helps!
Hello @tavlox The problem is probably in `len(whole_data.loc[:"2022"]`. If you are using `.iloc` you should use an `int` to access position 2022, not `"2022"`. With `.loc` it depends on your...
Hello @solegalli, The `create_predict_X` method will be available in skforecast `0.13.0`. 😄
Hello @amiroft, The main problem is that skforecast expects a regressor that follows the scikit-learn API. (X. y) As you can see in the error, our fit method internally calls...