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the Nixtla premium: NHiTS from `neuralforecast` consistently outperforming NHiTS from `pytorch-forecasting` in the wild lol

it's not a perfect comparison bc most of the hparams don't line up quite right between the two libraries but it is definitely noticeable

wondering if it has to do with your handling of lagged regressors, the `ptfc` version seems to limit you to lagged regressors from the target variable only

Hi <@U03DDFKMT0X>

That is cool, and as you noticed: NeuralForecast's NHITS allows for future, lagged and static exogenous variables.
We made these exogenous variables available for all the models that we host.

In the NBEATSx paper, exogenous variables gave us an advantage of 25% over the original NBEATS model.