azul

Hi @alan00775! Thanks for your suggestion. We are exploring options to reduce the time associated with compilation. For the [`ets`](https://github.com/Nixtla/statsforecast/blob/a250036faba7da91129e8227342948aefa11c499/statsforecast/ets.py#L29) model, you can set the environment variables `NOGIL='True'` and `CACHE='True'`....

Hi @dluuo! It was a design choice to work correctly with the `StatsForecast` class. But the `naive` model doesn't use the external regressors. We are working on refactoring the code...

Hi @dluuo. v1.0.0 address this issue.

Hi @shravankoninti, @colorado-mike! Usually, models such as `AutoARIMA` and `ETS` tend to have problems with intermittent data (mainly because of the zeros associated with this type of data). A preprocessing...

Nice @colorado-mike. I'm closing the issue. Feel free to reopen it if necessary.

Hi @goodwanghan! Do you need some help? :)

Released in v1.0.0.

Hi @baggiponte @ThomasBourgeois. The notebook was renamed: https://github.com/Nixtla/statsforecast/blob/main/nbs/examples/AutoArima_vs_Prophet.ipynb. Instructions on how to contribute can be found here: https://github.com/Nixtla/statsforecast/blob/main/CONTRIBUTING.md :)

Hi @tackes! Would it be possible for you to share with us any time series that gives you the error? To try to replicate and debug it :)

Hey @goodwanghan! As we have changed the model's name as of v1.0.0, this fix is no longer necessary. Thank you!