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#neural-forecast
Title
# neural-forecast
c

Chris Gervais

02/07/2023, 5:20 PM
i noticed in https://nixtla.github.io/neuralforecast/examples/exogenous_variables.html that the
weekday
column doesn't get hot encoded when passing to
futur_exog_list = [..., 'weekday']
- was that intentional? we're trying to integrate tsfresh features but seem to be running into issues when we hot encode - still investigating but thought it might be related to exogenous scaling
c

Cristian (Nixtla)

02/07/2023, 5:45 PM
Hi @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?
You can set scaler_type to None to remove scaling, to identify if this is the issue.
c

Chris Gervais

02/07/2023, 5:48 PM
sure 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
weekday
doesn't get hot encoded before passing it to the model
c

Cristian (Nixtla)

02/07/2023, 5:56 PM
In this example we didn't one hot encoded the variable, but it should work (run) in both cases
c

Chris Gervais

02/08/2023, 8:40 PM
circling 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?
example: 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
moving this to general chat to avoid overthreading this haha sorry
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