Merge pull request #225450 from EricWrightAtWork/how-to-forecasting-sdkv2

Migrate AutoML forecasting how-to guide to SDKv2

Author: JamesJBarnett

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

@@ -27,7 +27,7 @@ Naive, Seasonal Naive, Average, Seasonal Average | Time series | No sweeping wit
 Exponential Smoothing, ARIMA(X) | Time series | Grid search for within-class sweeping
 Prophet | Regression | No sweeping within class
 Linear SGD, LARS LASSO, Elastic Net, K Nearest Neighbors, Decision Tree, Random Forest, Extremely Randomized Trees, Gradient Boosted Trees, LightGBM, XGBoost | Regression | AutoML's [model recommendation service](https://www.microsoft.com/research/publication/probabilistic-matrix-factorization-for-automated-machine-learning/) dynamically explores hyper-parameter spaces
-Temporal Convolutional Network | Regression | Static list of models followed by random search over network size, dropout ratio, and learning rate.
+ForecastTCN | Regression | Static list of models followed by random search over network size, dropout ratio, and learning rate.
 
 For a description of the different model types, see the [forecasting models](./concept-automl-forecasting-methods.md#forecasting-models-in-automl) section of the methods overview article.
 
@@ -51,7 +51,7 @@ AutoML has two validation configurations - cross-validation and explicit validat
 
 AutoML follows the usual cross-validation procedure, training a separate model on each fold and averaging validation metrics from all folds. 
 
-Cross-validation for forecasting jobs is configured by setting the number of cross-validation folds and, optionally, the number of time periods between two consecutive cross-validation folds. See the [training and validation data](./how-to-auto-train-forecast.md#training-and-validation-data) guide for more information and an example of configuring cross-validation for forecasting.
+Cross-validation for forecasting jobs is configured by setting the number of cross-validation folds and, optionally, the number of time periods between two consecutive cross-validation folds. See the [custom cross-validation settings](./how-to-auto-train-forecast.md#custom-cross-validation-settings) guide for more information and an example of configuring cross-validation for forecasting.
 
 You can also bring your own validation data. Learn more in the [configure data splits and cross-validation in AutoML](how-to-configure-cross-validation-data-splits.md#provide-validation-data) article.
 

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

@@ -135,7 +135,7 @@ Guardrail|Status|Condition for trigger
 
 You can customize your featurization settings to ensure that the data and features that are used to train your ML model result in relevant predictions.
 
-To customize featurizations, specify `"featurization": FeaturizationConfig` in your `AutoMLConfig` object. If you're using the Azure Machine Learning studio for your experiment, see the [how-to article](how-to-use-automated-ml-for-ml-models.md#customize-featurization). To customize featurization for forecastings task types, refer to the [forecasting how-to](how-to-auto-train-forecast.md#customize-featurization).
+To customize featurizations, specify `"featurization": FeaturizationConfig` in your `AutoMLConfig` object. If you're using the Azure Machine Learning studio for your experiment, see the [how-to article](how-to-use-automated-ml-for-ml-models.md#customize-featurization). To customize featurization for forecastings task types, refer to the [forecasting how-to](v1/how-to-auto-train-forecast-v1.md#customize-featurization).
 
 Supported customizations include:
 

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

@@ -151,7 +151,7 @@
               href: concept-automated-ml.md
             - name: Time series forecasting
               items:
-              - name: Overview of forecasting methods
+              - name: Forecasting methods
                 displayName: auto ML, time series, forecasting, methods
                 href: concept-automl-forecasting-methods.md
               - name: Model sweeping and selection
@@ -552,6 +552,14 @@
             - name: Auto-train a natural language processing model
               displayName: nlp, auto ML, automl, SDK
               href: how-to-auto-train-nlp-models.md
+            - name: Time series forecasting
+              items:
+              - name: Auto-train a forecasting model (Python)
+                displayName: forecasting, time series, auto ML, automl, SDK 
+                href: how-to-auto-train-forecast.md
+              - name: Frequently asked questions
+                displayName: auto ML, time series, forecasting, FAQ
+                href: how-to-automl-forecasting-faq.md
             - name: Understand charts and metrics
               href: how-to-understand-automated-ml.md
             - name: Use ONNX model in .NET application