When using

predict_insample

I get a warning message for some of the algos (e.g., LSTM, RNN) that the in sample preds may not be accurate due to the scaler (e.g.,

WARNING: Predict insample might not provide accurate predictions for recurrent model LSTM class yet due to scaling.

). Is this message still accurate? Does it only apply to RNN-based models (that's what it looks like from what I'm seeing in the warnings)?

Hi @Brian Head, 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 @Cristian (Nixtla). 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?