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Hello Pandula, thanks for joining! All the models in our neuralforecast library are multi-step. The hyperparameter n_time_out controls the forecast horizon. I recommend trying the N-HiTS or RNN models first. They are univariate, but can be used for multivariate problems as well since they will share parameters for all the ts (one model will be used to forecast all ts). Check our colab example: https://colab.research.google.com/drive/1WjBbQzaivQhOldGolzymOtLmo6QX4Ieg#scrollTo=HXKT2-fpUD0Z. We use the N-HiTS model in a multivariate multi-step problem.

NeuralForecast's models take as input your target data with all your series

Y_df

and exogenous data

X_df

with columns

unique_id, ds

hey thank you both for the help and suggestion. I tried it and got it to work, so that’s a win! I am trying to figure out how to work with

X_df

now, specially how to structure it. I guess

unique_id

is used to differentiate between different series? If I keep the

ds

and

unique_id

columns plus all other exogenous vars in the

X_df

, it would work right? (apologies for not trying it out and asking, it’s pretty late and I only saw this message now) I scanned through the notebooks in

/nbs

and saw examples, would they be the best source of documentation to refer to?

each ts is separately forecasted

Thanks @Cristian (Nixtla)!