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I'm following this tutorial to try Machine Learning AutoML Forecasting.

In the several parameters we can submit to the AutoML experiment, we have these ones:

  1. target_logs;
  2. target_rolling_window_size;

Can you explain with an example how the several forecasting algorithms works when these two parameters are set?

Thank you

automl_advanced_settings = {
    'time_column_name': time_column_name,
    'max_horizon': max_horizon,
    'target_lags': 12,
    'target_rolling_window_size': 4,
}
automl_config = AutoMLConfig(task='forecasting',                             
                             primary_metric='normalized_root_mean_squared_error',                  

                             experiment_timeout_hours=0.3,
                             training_data=train,
                             label_column_name=target_column_name,
                             compute_target=compute_target,
                             enable_early_stopping = True,
                             n_cross_validations=3,                             
                             verbosity=logging.INFO,
                            **automl_advanced_settings)
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