fix nb links

Author: EricWrightAtWork

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

@@ -313,7 +313,7 @@ returned_job.services["Studio"].endpoint
 
 ## Forecasting with a trained model
 
-Once you've used to AutoML to train and select a best model, the next step is to evaluate the model. If it meets your requirements, you can use it to generate forecasts into the future. This section shows how to write Python scripts for evaluation and prediction (inference). For an example of deploying a trained model with an inference script, see our [example notebook](~/azureml-examples-main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb).
+Once you've used to AutoML to train and select a best model, the next step is to evaluate the model. If it meets your requirements, you can use it to generate forecasts into the future. This section shows how to write Python scripts for evaluation and prediction (inference). For an example of deploying a trained model with an inference script, see our [example notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb).
 
 ### Evaluating model accuracy with a rolling forecast
 
@@ -375,11 +375,11 @@ def run(mini_batch):
     return pd.concat(resultList, sort=False, ignore_index=True)
 ```
 
-In this sample, the step size for the rolling forecast is set to one which means that the forecaster is advanced one period, or one day in our demand prediction example, at each iteration. The total number of forecasts returned by `rolling_forecast` depends on the length of the test set and this step size. For more details and examples, see the [rolling_forecast() documentation](/python/api/azureml-training-tabular/azureml.training.tabular.models.forecasting_pipeline_wrapper_base.forecastingpipelinewrapperbase#azureml-training-tabular-models-forecasting-pipeline-wrapper-base-forecastingpipelinewrapperbase-rolling-forecast) and the [Forecasting away from training data notebook](~/azureml-examples-main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb). 
+In this sample, the step size for the rolling forecast is set to one which means that the forecaster is advanced one period, or one day in our demand prediction example, at each iteration. The total number of forecasts returned by `rolling_forecast` depends on the length of the test set and this step size. For more details and examples, see the [rolling_forecast() documentation](/python/api/azureml-training-tabular/azureml.training.tabular.models.forecasting_pipeline_wrapper_base.forecastingpipelinewrapperbase#azureml-training-tabular-models-forecasting-pipeline-wrapper-base-forecastingpipelinewrapperbase-rolling-forecast) and the [Forecasting away from training data notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb). 
 
 ### Prediction into the future
 
-The [forecast_quantiles()](/python/api/azureml-train-automl-client/azureml.train.automl.model_proxy.modelproxy#forecast-quantiles-x-values--typing-any--y-values--typing-union-typing-any--nonetype----none--forecast-destination--typing-union-typing-any--nonetype----none--ignore-data-errors--bool---false-----azureml-data-abstract-dataset-abstractdataset) function allows specifications of when predictions should start, unlike the `predict()` method, which is typically used for classification and regression tasks. The forecast_quantiles() method, by default, generates a point forecast that doesn't have a cone of uncertainty around it. Learn more in the [Forecasting away from training data notebook](~/azureml-examples-main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb).
+The [forecast_quantiles()](/python/api/azureml-train-automl-client/azureml.train.automl.model_proxy.modelproxy#forecast-quantiles-x-values--typing-any--y-values--typing-union-typing-any--nonetype----none--forecast-destination--typing-union-typing-any--nonetype----none--ignore-data-errors--bool---false-----azureml-data-abstract-dataset-abstractdataset) function allows specifications of when predictions should start, unlike the `predict()` method, which is typically used for classification and regression tasks. The forecast_quantiles() method, by default, generates a point forecast that doesn't have a cone of uncertainty around it. Learn more in the [Forecasting away from training data notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb).
 
 In the following example, you first replace all values in `y_pred` with `NaN`. The forecast origin is at the end of training data in this case. However, if you replaced only the second half of `y_pred` with `NaN`, the function would leave the numerical values in the first half unmodified, but forecast the `NaN` values in the second half. The function returns both the forecasted values and the aligned features.
 
@@ -440,7 +440,7 @@ The following diagram shows the workflow for the many models solution.
 
 ![Many models concept diagram](./media/how-to-auto-train-forecast/many-models.svg)
 
-The following code demonstrates the key parameters users need to set up their many models run. See the [Many Models- Automated ML notebook](~/azureml-examples-main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-many-models/auto-ml-forecasting-many-models.ipynb) for a many models forecasting example 
+The following code demonstrates the key parameters users need to set up their many models run. See the [Many Models- Automated ML notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-many-models/auto-ml-forecasting-many-models.ipynb) for a many models forecasting example 
 
 ```python
 from azureml.train.automl.runtime._many_models.many_models_parameters import ManyModelsTrainParameters
@@ -477,7 +477,7 @@ To further visualize this, the leaf levels of the hierarchy contain all the time
 
 The hierarchical time series solution is built on top of the Many Models Solution and share a similar configuration setup.
 
-The following code demonstrates the key parameters to set up your hierarchical time series forecasting runs. See the [Hierarchical time series- Automated ML notebook](~/azureml-examples-main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb), for an end to end example. 
+The following code demonstrates the key parameters to set up your hierarchical time series forecasting runs. See the [Hierarchical time series- Automated ML notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb), for an end to end example. 
 
 ```python
 
@@ -519,11 +519,11 @@ hts_parameters = HTSTrainParameters(
 
 ## Example notebooks
 
-See the [forecasting sample notebooks](~/azureml-examples-main/sdk/python/jobs/automl-standalone-jobs) for detailed code examples of advanced forecasting configuration including:
+See the [forecasting sample notebooks](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs) for detailed code examples of advanced forecasting configuration including:
 
-* [deep learning models](~/azureml-examples-main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb)
-* [holiday detection and featurization](~/azureml-examples-main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/auto-ml-forecasting-bike-share.ipynb)
-* [manual configuration for lags and rolling window aggregation features](~/azureml-examples-main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb)
+* [deep learning models](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb)
+* [holiday detection and featurization](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/auto-ml-forecasting-bike-share.ipynb)
+* [manual configuration for lags and rolling window aggregation features](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb)