Merge pull request #100313 from Aniththa/patch-32

Update how-to-auto-train-forecast.md

Author: PRMerger6

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

@@ -124,7 +124,7 @@ The [`AutoMLConfig`](https://docs.microsoft.com/python/api/azureml-train-automl-
 
 See the [reference documentation](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig) for more information.
 
-Create the time-series settings as a dictionary object. Set the `time_column_name` to the `day_datetime` field in the data set. Define the `grain_column_names` parameter to ensure that **two separate time-series groups** are created for the data; one for store A and B. Lastly, set the `max_horizon` to 50 in order to predict for the entire test set. Set a forecast window to 10 periods with `target_rolling_window_size`, and specify a single lag on the target values for two periods ahead with the `target_lags` parameter.
+Create the time-series settings as a dictionary object. Set the `time_column_name` to the `day_datetime` field in the data set. Define the `grain_column_names` parameter to ensure that **two separate time-series groups** are created for the data; one for store A and B. Lastly, set the `max_horizon` to 50 in order to predict for the entire test set. Set a forecast window to 10 periods with `target_rolling_window_size`, and specify a single lag on the target values for two periods ahead with the `target_lags` parameter. It is recommended to set `max_horizon`, `target_rolling_window_size` and `target_lags` to "auto" which will automatically detect these values for you. In the example below, "auto" settings have been used for these paramaters. 
 
 ```python
 time_series_settings = {