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# neural-forecasts
Slackbot
02/07/2023, 5:20 PMThis message was deleted.
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Cristian (Nixtla)
02/07/2023, 5:45 PMHi @Chris Gervais! Yes, this is by design. All features should be pre-processed before passing them to the models. Can you provide more details on what issues do you have with the encoded features?
Cristian (Nixtla)
02/07/2023, 5:47 PMYou can set scaler_type to None to remove scaling, to identify if this is the issue.
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Chris Gervais
02/07/2023, 5:48 PMsure yah, so for example we're taking a column of data representing hour of day (1-24) and converting those to dummy variables via hot encoding. but in the example re: exogenous the variable
weekdayc
Cristian (Nixtla)
02/07/2023, 5:56 PMIn this example we didn't one hot encoded the variable, but it should work (run) in both cases
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Chris Gervais
02/08/2023, 8:40 PMcircling back on this - it wasn't the encoding, we had gaps in our y values. but that brings me to another question - is it possible to create a future df that doesn't rely on "current" y values?
Chris Gervais
02/08/2023, 8:43 PMexample: fitting to 2022 hourly data and using it to forecast Feb 9th 2023, without having access to y values from Jan 1 - Feb 8th 2023
Chris Gervais
02/08/2023, 8:50 PMmoving this to general chat to avoid overthreading this haha sorry
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