Hi, how can I forecast predictions for new data (without having

y

values)? In the documentation it says that I can do this using

futr_df

and supplying to it "DataFrame with [

unique_id

,

ds

]", however whenever I try to predict with

nf.predict(futr_df=futr_df)

with

futr_df

having only

unique_id

and

ds

columns it gives all NaNs (and when I add

y

column it works fine, but obviously at prediction time you don't have the

y

column)

Hi @Ada Puślecka. The

futr_df

its only for the future exogenous variables. To pass new data, use the

df

argument of the

predict

method. The forecasts will correspond to the future after the last timestamps of the new data.

df

should contain historic values of all the variables used during training, including the target variable

y

and historic exogenous covariates.

ah got it, so I don't need to provide a target variable for the period I am forecasting, but just for the previous timesteps so the model can use it for forecasting, thanks. I have one more confusion around forecasting multiple time series at once - is is possible to train the model on set of 10 time series, but then at prediction to forecast only 5 of them because some of the time series finished or do I need to provide all the time series and just pad the series which finished with zeros?

You dont need to add all the time series during prediction for univariate models (all except

StemGNN

). Since the

unique_id

is not used by the model, you can pass any new data you want, including new time series.