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

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?

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

In this example we didn't one hot encoded the variable, but it should work (run) in both cases

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?