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I was going through the literature for time-series forecasting using DL and all the methods I read about only use the variable of interest at previous timesteps to predict the same variable at time step (t+1). I didn't find any method that includes covariates information in one way or the other except in the Temporal Fusion Transformer.

Can anyone suggest to me some models/articles that include covariates information in deep learning models preferably?

Thanks in advance

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So this isn't strictly about deep learning models or any applications to time series forecasting, but a more general approach to handle covariates below.

https://github.com/felipemaiapolo/infoselect

The link includes a python package based off three cited journal articles on how the authors using Gaussian Mixture Models to handle this issue. It looks like this could apply to your situation.

Let me know if that helps.

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