update external section refs

Author: GitHubber17

articles/machine-learning/concept-automl-forecasting-methods.md

@@ -92,7 +92,7 @@ where $H$ is the forecast horizon, $l_{\text{max}}$ is the maximum lag order, an
 
  $T_{\text{CV}} = 2H + (n_{\text{CV}} - 1) n_{\text{step}} + \text{max}(l_{\text{max}}, s_{\text{window}}) + 1$,
 
-where $n_{\text{CV}}$ is the number of cross-validation folds and $n_{\text{step}}$ is the CV step size, or offset between CV folds. The basic logic behind these formulas is that you should always have at least a horizon of training observations for each time series, including some padding for lags and cross-validation splits. See [forecasting model selection](./concept-automl-forecasting-sweeping.md#model-selection) for more details on cross-validation for forecasting.
+where $n_{\text{CV}}$ is the number of cross-validation folds and $n_{\text{step}}$ is the CV step size, or offset between CV folds. The basic logic behind these formulas is that you should always have at least a horizon of training observations for each time series, including some padding for lags and cross-validation splits. See [forecasting model selection](./concept-automl-forecasting-sweeping.md#model-selection-in-automl) for more details on cross-validation for forecasting.
 
 ### Missing data handling
 AutoML's time series models require regularly spaced observations in time. Regularly spaced, here, includes cases like monthly or yearly observations where the number of days between observations may vary. Prior to modeling, AutoML must ensure there are no missing series values _and_ that the observations are regular. Hence, there are two missing data cases:

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

@@ -131,7 +131,7 @@ Add more detail to this configuration in subsequent sections of this how-to guid
 
 ---
 
-You specify [validation data](concept-automated-ml.md#training-validation-and-test-data) in a similar way. Create a MLTable and specify a validation data input. Alternatively, if you don't supply validation data, AutoML automatically creates cross-validation splits from your training data to use for model selection. For more information, see [forecasting model selection](./concept-automl-forecasting-sweeping.md#model-selection). For more information about how much training data you need, see [training data length requirements](./concept-automl-forecasting-methods.md#data-length-requirements).
+You specify [validation data](concept-automated-ml.md#training-validation-and-test-data) in a similar way. Create a MLTable and specify a validation data input. Alternatively, if you don't supply validation data, AutoML automatically creates cross-validation splits from your training data to use for model selection. For more information, see [forecasting model selection](./concept-automl-forecasting-sweeping.md#model-selection-in-automl). For more information about how much training data you need, see [training data length requirements](./concept-automl-forecasting-methods.md#data-length-requirements).
 
 Learn more about how AutoML applies cross validation to [prevent over fitting](concept-manage-ml-pitfalls.md#prevent-overfitting).
 
@@ -587,7 +587,7 @@ forecasting:
 
 #### Custom cross-validation settings
 
-There are two customizable settings that control cross-validation for forecasting jobs: the number of folds, `n_cross_validations`, and the step size defining the time offset between folds, `cv_step_size`. For more information on the meaning of these parameters, see [forecasting model selection](./concept-automl-forecasting-sweeping.md#model-selection).
+There are two customizable settings that control cross-validation for forecasting jobs: the number of folds, `n_cross_validations`, and the step size defining the time offset between folds, `cv_step_size`. For more information on the meaning of these parameters, see [forecasting model selection](./concept-automl-forecasting-sweeping.md#model-selection-in-automl).
 
 By default, AutoML sets both settings automatically based on characteristics of your data. Advanced users might want to set them manually. For example, suppose you have daily sales data and you want your validation setup to consist of five folds with a seven-day offset between adjacent folds. The following code sample shows how to set these values:
 

articles/machine-learning/how-to-automl-forecasting-faq.md

@@ -116,7 +116,7 @@ To choose between them, note that NRMSE penalizes outliers in the training data
 
 ## Will AutoML always select the same best model from the same training data and configuration?
 
-[AutoML's model search process](./concept-automl-forecasting-sweeping.md#model-sweeping) is not deterministic, so it doesn't always select the same model from the same data and configuration.  
+[AutoML's model search process](./concept-automl-forecasting-sweeping.md#model-sweeping-in-automl) is not deterministic, so it doesn't always select the same model from the same data and configuration.  
 
 ## How do I fix an out-of-memory error?