The Improving Forecast Accuracy with Machine Learning solution is designed to help organizations that rely on generating accurate forecasts and store historical demand time series data. Whether organizations are developing forecasts for the first time, or optimizing their current pipeline, this solution will reduce the overhead cost of generating forecasts from time series data, related time series data, and item metadata.
This solution supports multiple forecasts and per-forecast parameter configuration in order to reduce the repetitive task of generating multiple forecasts. The use of an AWS Step Function eliminates the undifferentiated heavy lifting of creating Amazon Forecast datasets, dataset groups, predictors, and forecasts—allowing developers and data scientists to focus on the accuracy of their forecasts. Amazon Forecast predictors and forecasts can be updated as item demand data, related timeseries data, and item metadata are refreshed, which allows for A/B testing against different sets of related timeseries data and item metadata.
After forecast exports and Athena tables are created, the solution automatically creates an interactive and shareable analysis in Amazon QuickSight with pre-set visuals and configurations to analyze your forecasts.
To better capture and alert users of data quality issues, a configurable alert function can also be deployed with Amazon Simple Notification Service (Amazon SNS). This notifies the user on success and failure of the automated forecasting job, reducing the need for users to monitor their forecast workflow.
This guide provides infrastructure and configuration information for planning and deploying the solution in AWS.
The following describes the architecture of the solution:
The AWS CloudFormation template deploys the resources required to automate your Amazon Forecast usage and deployments. Based on the capabilities of the solution, the architecture is divided into three parts: Data Preparation, Forecasting, and Data Visualization. The template includes the following components:
- An Amazon Simple Storage Service (Amazon S3) bucket for Amazon Forecast configuration where you specify configuration settings for your dataset groups, datasets, predictors and forecasts, as well as the datasets themselves.
- An Amazon S3 event notification that triggers when new datasets are uploaded to the related Amazon S3 bucket.
- An AWS Step Functions State Machine. This combines a series of AWS Lambda functions that build, train. and deploy your Machine Learning (ML) models in Amazon Forecast.
- An Amazon Simple Notification Service (Amazon SNS) topic and email subscription that notify an administrator user with the results of the AWS Step Function.
- An optional Amazon SageMaker Notebook Instance that data scientists and developers can use to prepare and process data, and evaluate your Forecast output.
- An AWS Glue Job that consolidates your input data and forecast data into a consolidated view that can be queried with standard SQL queries using Amazon Athena.
- An Amazon QuickSight analysis is deployed to help visualize forecast output.
Note: Upgrading to 1.4.0 from earlier versions is not supported. Please redeploy the stack and copy your your configuration and data to the newly created forecast data bucket.
Note: From v1.2.0, all AWS CloudFormation template resources are created by the AWS CDK and AWS Solutions Constructs. Stateful CloudFormation template resources maintain the same logical ID comparing to v1.2.0, making the solution upgradable in place.
AWS CDK Solutions Constructs make it easier to consistently create well-architected applications. All AWS Solutions Constructs are reviewed by AWS and use best practices established by the AWS Well-Architected Framework. This solution uses the following AWS CDK Solutions Constructs:
You can launch this solution with one click from AWS Solutions Implementations.
To customize the solution, or to contribute to the solution, follow the steps below:
The following procedures assumes that all the OS-level configuration has been completed. They are:
- AWS Command Line Interface
- Python 3.8 or later
Clone this git repository
git clone https://github.com/aws-solutions/<repository_name>
Follow the steps in source/infrastructure/README.md (here) to use CDK to deploy the
solution
To test the solution, or provide a demo - you can follow the synthetic data generation instructions in
source/synthetic/README.md (here).
This solution collects anonymous operational metrics to help AWS improve the quality of features of the solution. For more information, including how to disable this capability, please see the implementation guide.
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.