text: Title: [Help] Global Multivariate Forecasting
  Body: Hey there,
  
  I'm having trouble with the global models, especially how to prepare the data and what to use for predictions. Maybe someone can point me in the right direction. I'm using the Walmart dataset from Kaggle right now.
  
  What I've done so far is split the data before grouping with **from_group_dataframe** and then make separate time series grouped by "store" for each target and the convariates for both training and testing sets and scale them.
  
  Problems seem to start with the scaler; when plotting both training and testing, in the end they both seem to be scaled differently. Am I supposed to fit the scaler on the whole dataset before splitting?
  
  Also, regarding **past_covariates** in **predict** should this include the data for both the training and testing sets? When using the test set for the covariates (x_test), I've received an error that the data does not go back far enough.
  
  For the series used in **predict**, I have already figured out that this needs to be the training set.
  
  Big thanks for this package; the available models and data prep options look really nice, and I can't wait to try out more different things once I get this to work 👍 
  
  
  ```python
  
  df_train = df.loc[df["Date"] < train_test_split_date]
  df_test = df.loc[df["Date"] >= train_test_split_date]
  
  y_train = TimeSeries.from_group_dataframe(df_train, group_cols="Store", time_col="Date", value_cols=["Weekly_Sales"],
                                           freq="W")
  x_train = TimeSeries.from_group_dataframe(df_train, group_cols="Store", time_col="Date", value_cols=features, freq="W")
  y_test = TimeSeries.from_group_dataframe(df_test , group_cols="Store",time_col="Date", value_cols=["Weekly_Sales"],
                                           freq="W")
  x_test = TimeSeries.from_group_dataframe(df_test , group_cols="Store", value_cols=features, freq="W")
  
  y_scaler_dataset = Scaler()
  x_scaler_dataset = Scaler()
  
  y_train = y_scaler_dataset.fit_transform(y_train)
  x_train = x_scaler_dataset.fit_transform(x_train)
  
  y_test = y_scaler_dataset.fit_transform(y_test)
  x_test = x_scaler_dataset.fit_transform(y_test)
  
  forecast_steps=10
  epochs=200
  
  model_BlockRNNModel = BlockRNNModel(
       model="RNN",
       input_chunk_length=15,
       output_chunk_length=forecast_steps,
       n_epochs=epochs,
       random_state=0,
   )
  
  model_BlockRNNModel.fit(
      series=y_train,
      past_covariates=x_train,
      verbose=True,
  )
  
  pred_cov = model_BlockRNNModel.predict(n=forecast_steps, series=y_train, past_covariates=x_train )
  }
  
  y_test = y_scaler_dataset.inverse_transform(y_test)
  y_train = y_scaler_dataset.inverse_transform(y_train )
  pred_cov = y_scaler_dataset.inverse_transform(pred_cov)
  
  ```