Merge pull request aws#155 from lostella/master

added deepar_synthetic notebook

Author: djarpin

README.md

@@ -29,6 +29,7 @@ These examples provide quick walkthroughs to get you up and running with Amazon
 - [Image Classification](introduction_to_amazon_algorithms/imageclassification_caltech) includes full training and transfer learning examples of Amazon SageMaker's Image Classification algorithm.  This uses a ResNet deep convolutional neural network to classify images from the caltech dataset.
 - [XGBoost for regression](introduction_to_amazon_algorithms/xgboost_abalone) predicts the age of abalone ([Abalone dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html)) using regression from Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost).
 - [XGBoost for multi-class classification](introduction_to_amazon_algorithms/xgboost_mnist) uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to classify handwritten digits from the MNIST dataset as one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented.
+- [DeepAR for time series forecasting](introduction_to_amazon_algorithms/deepar_synthetic) illustrates how to use the Amazon SageMaker DeepAR algorithm for time series forecasting on a synthetically generated data set.
 
 ### Scientific Details of Algorithms
 
@@ -42,7 +43,7 @@ These examples that showcase unique functionality available in Amazon SageMaker.
 
 - [Data Distribution Types](advanced_functionality/data_distribution_types) showcases the difference between two methods for sending data from S3 to Amazon SageMaker Training instances.  This has particular implication for scalability and accuracy of distributed training.
 - [Encrypting Your Data](advanced_functionality/handling_kms_encrypted_data) shows how to use Server Side KMS encrypted data with Amazon SageMaker training. The IAM role used for S3 access needs to have permissions to encrypt and decrypt data with the KMS key.
-- [Using Parquet Data](advanced_functionality/parquet_to_recordio_protobuf) shows how to bring [Parquet](https://parquet.apache.org/) data sitting in S3 into an Amazon SageMaker Notebook and convert it into the recordIO-protobuf format that many SageMaker algorithms consume. 
+- [Using Parquet Data](advanced_functionality/parquet_to_recordio_protobuf) shows how to bring [Parquet](https://parquet.apache.org/) data sitting in S3 into an Amazon SageMaker Notebook and convert it into the recordIO-protobuf format that many SageMaker algorithms consume.
 - [Connecting to Redshift](advanced_functionality/working_with_redshift_data) demonstrates how to copy data from Redshift to S3 and vice-versa without leaving Amazon SageMaker Notebooks.
 - [Bring Your Own XGBoost Model](advanced_functionality/xgboost_bring_your_own_model) shows how to use Amazon SageMaker Algorithms containers to bring a pre-trained model to a realtime hosted endpoint without ever needing to think about REST APIs.
 - [Bring Your Own k-means Model](advanced_functionality/kmeans_bring_your_own_model) shows how to take a model that's been fit elsewhere and use Amazon SageMaker Algorithms containers to host it.

introduction_to_amazon_algorithms/README.md

@@ -14,3 +14,4 @@ These examples provide quick walkthroughs to get you up and running with Amazon
 - [Image Classification](introduction_to_amazon_algorithms/imageclassification_caltech) includes full training and transfer learning examples of Amazon SageMaker's Image Classification algorithm.  This uses a ResNet deep convolutional neural network to classify images from the caltech dataset.
 - [XGBoost for regression](xgboost_abalone) predicts the age of abalone ([Abalone dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html)) using regression from Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost).
 - [XGBoost for multi-class classification](xgboost_mnist) uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to classifiy handwritten digits from the MNIST dataset as one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented.
+- [DeepAR for time series forecasting](deepar_synthetic) illustrates how to use the Amazon SageMaker DeepAR algorithm for time series forecasting on a synthetically generated data set.