Going through this tutorial on exogenous variables https://nixtla.github.io/neuralforecast/examples/exogenous_variables.html In this example the

df

contains two

unique_ids

. When the model is fitted, is it building a global model? By global, I would interpret that the model learns from all the different time series (or

unique_ids

) in order to perform the forecast for each

unique_id

?

df = pd.read_csv('<https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE.csv>')
df['ds'] = pd.to_datetime(df['ds'])
df.head()

horizon = 24 # day-ahead daily forecast
models = [NHITS(h = horizon,
                input_size = 5*horizon,
                futr_exog_list = ['gen_forecast', 'week_day'], # <- Future exogenous variables
                hist_exog_list = ['system_load'], # <- Historical exogenous variables
                stat_exog_list = ['market_0', 'market_1'], # <- Static exogenous variables
                scaler_type = 'robust')]

nf = NeuralForecast(models=models, freq='H')
nf.fit(df=df,
       static_df=static_df)

Hey. Yes, neuralforecast trains global models using all series

@José Morales thanks for the confirmation!