Multivariate input to predict univariate variables

[ricokoff]

Hi,

I was wondering if it is possible to use your model with a multivariate input while predicting a univariate variable. If not, do you know what code I should change to make it work? As you are using some of the code from Informer, I was thinking about using the 'MS' features parameter, but this gives the following error in the encoder on line 109:

level = level.view(b, t, self.c_out, 1)
RuntimeError: shape '[32, 192, 1, 1]' is invalid for input of size 36864

Now I could reshape this level variable so it would be consistent with my data, but I don't know if your model is capable of handling that. Please let me know what you think.

Thanks for your time and contribution,

Rico

[gorold]

Hi, thanks for your interest in our work, one easy workaround to get univariate output variables will be to mask the unwanted dimensions in the output forecast.

[ricokoff]

Thanks for the response. This is indeed what I tried at first, but I suspected that it would make the training process a lot slower. However, if this is the only way then I will try it anyway with more computing time.