Nixtla's Open Source Time Series Ecosystem.

Nixtla Community

Hi <@U05RXV4UY4R>, yes it still applies if you are using `scaler_type` to scale the data, and only for RNN methods. It will still return values, but we have seen that because of the way we are scaling, we have seen some performance degradation for the predict insample. If you scale the time series before (with the core class, see this tutorial: <https://nixtla.github.io/neuralforecast/examples/time_series_scaling.html> ) then this warning does not apply.

Thank you again <@U03619XRWKD>. I'm now having a different issue with `predict_insample`. I started getting an error
```ValueError: could not broadcast input array from shape (48384,9) into shape (48222,9)```
after making the scaling change. It still happens after switching back to what I had. I can't find anything else I've changed. Any ideas?

BTW, `cross_validation` and `fit` both work as expected. It's only a problem with the `predict_insample`.