Updated: DeepAR mention in Linear Time Series Notebook (aws#157)

Author: djarpin

introduction_to_applying_machine_learning/linear_time_series_forecast/linear_time_series_forecast.ipynb

@@ -33,7 +33,7 @@
     "\n",
     "Properly modeling time-series data takes a great deal of care.  What's the right level of aggregation to model at?  Too granular and the signal gets lost in the noise, too aggregate and important variation is missed.  Also, what is the right cyclicality?  Daily, weekly, monthly?  Are there holiday peaks?  How should we weight recent versus overall trends?\n",
     "\n",
-    "Linear regression with appropriate controls for trend, seasonality, and recent behavior, remains a common method for forecasting stable time-series with reasonable volatility.  This notebook will build a linear model to forecast weekly output for US gasoline products starting in 1991 to 2005.  It will focus almost exclusively on the application.  For a more in-depth treatment on forecasting in general, see [Forecasting: Principles & Practice](https://robjhyndman.com/uwafiles/fpp-notes.pdf).\n",
+    "Linear regression with appropriate controls for trend, seasonality, and recent behavior, remains a common method for forecasting stable time-series with reasonable volatility.  This notebook will build a linear model to forecast weekly output for US gasoline products starting in 1991 to 2005.  It will focus almost exclusively on the application.  For a more in-depth treatment on forecasting in general, see [Forecasting: Principles & Practice](https://robjhyndman.com/uwafiles/fpp-notes.pdf).  In addition, because our dataset is a single time-series, we'll stick with SageMaker's Linear Learner algorithm.  If we had multiple, related time-series, we would use SageMaker's DeepAR algorithm, which is specifically designed for forecasting.  See the [DeepAR Notebook](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/deepar_synthetic/deepar_synthetic.ipynb) for more detail.\n",
     "\n",
     "---\n",
     "\n",