Time series identifiers & featurization update

Two changes
1) time series identifiers does not have an Auto setting, instead if users simply don't specify the parameter, AutoML will intelligently detect it for you. You can still manually override for edge cases, but overtime this should just become "part of what AutoML does" and be more of a guardrail
2) Update the link at the end of the featurization section to point to the TimeIndexFeaturizer class instead which lists all the possible engineered features that can be generated for time series data

FYI @nibaccam

Author: cartacioS

articles/machine-learning/how-to-auto-train-forecast.md

@@ -77,7 +77,7 @@ Automated machine learning automatically tries different models and algorithms a
 
 ### Configuration settings
 
-Similar to a regression problem, you define standard training parameters like task type, number of iterations, training data, and number of cross-validations. Forecasting tasks require the `time_column_name` and `forecast_horizon`  parameters to configure your experiment. You can also include additional parameters to better configure your run, see the [optional configurations](#optional-configurations) section for more detail on what can be included.
+Similar to a regression problem, you define standard training parameters like task type, number of iterations, training data, and number of cross-validations. Forecasting tasks require the `time_column_name` and `forecast_horizon` parameters to configure your experiment. If the data includes multiple time series, such as sales data for multiple stores or energy data across different states, the `time_series_id_column_names` parameter will be automatically detected and set by automated ML. You can also include additional parameters to better configure your run, see the [optional configurations](#optional-configurations) section for more detail on what can be included.
 
 | Parameter name | Description |
 |-------|-------|
@@ -87,7 +87,6 @@ Similar to a regression problem, you define standard training parameters like ta
 The following code, 
 * Leverages the [`ForecastingParameters`](/python/api/azureml-automl-core/azureml.automl.core.forecasting_parameters.forecastingparameters) class to define the forecasting parameters for your experiment training
 * Sets the `time_column_name` to the `day_datetime` field in the data set. 
-* Defines the `time_series_id_column_names` parameter to `auto`. 
 * Sets the `forecast_horizon` to 50 in order to predict for the entire test set. 
 
 
@@ -96,7 +95,6 @@ from azureml.automl.core.forecasting_parameters import ForecastingParameters
 
 forecasting_parameters = ForecastingParameters(time_column_name='day_datetime', 
                                                forecast_horizon=50,
-                                               time_series_id_column_names='auto',
                                                freq='W')
 
 ```
@@ -141,7 +139,7 @@ However, the following steps are performed only for `forecasting` task types:
 * Create time-based features to assist in learning seasonal patterns
 * Encode categorical variables to numeric quantities
 
-To get a summary of what features are created as result of these steps, see [Featurization transparency](how-to-configure-auto-features.md#featurization-transparency)
+To view the full list of possible engineered features generated from time series data, see [TimeIndexFeaturizer Class](https://docs.microsoft.com/python/api/azureml-automl-runtime/azureml.automl.runtime.featurizer.transformer.timeseries.time_index_featurizer.timeindexfeaturizer?view=azure-ml-py).
 
 > [!NOTE]
 > Automated machine learning featurization steps (feature normalization, handling missing data,