Amazon Forecast is a machine learning service that allows you to build and scale time series models in a quick and effective process. The content below is designed to help you build out your first models for your given use case and makes assumptions that your data may not yet be in an ideal format for Amazon Forecast to use.
This repository assumes a base familiarity with the service and if you have not already done so it is recommended that you use the getting-started material below.
If you are not familiar with Amazon Forecast you can learn more about this tool on these pages:
The notebooks have been scrubbed of all output before usage, however if you'd like to see a fully worked out example of this process, explore the notebooks in the completed folder.
- Deploying Your Working Environment
- Validating and Importing Target Time Series Data
- Creating and Evaluating Your First Predictors
- Validating and Importing Related Time Series Data
- Creating and Evaluating Related Time Series Enabled Predictors
- Next Steps
That is a genereal order to this proccess, however if you are operating this as an assisted 2 day on-site POC. It is recommended that you try both data import steps beforehand. Once the related data has been successfully imported you can delete it so that it does not muddy the results from your first Predictor.
As mentioned above, the first step is to deploy a CloudFormation template that will perform much of the initial setup work for you. In another browser window or tab, login to your AWS account. Once you have done that, open the link below in a new tab to start the process of deploying the items you need via CloudFormation.
This CloudFormation template will compelte the following:
- Create a SageMaker Role for your POC
- Create a SageMaker Notebook Instance (and optional VPC configuration) for your POC
- Clone the POC codebase onto the Notebook Instance.
| Region | Deploy Link |
|---|---|
ap-southeast-1 (Singapore) |
 |
us-east-1 (US N. Virginia) |
|
Follow along with the screenshots below if you have any questions about deploying the stack.
Start by clicking Next at the bottom like this:
On this page you have a few tasks:
- Change the Stack name to something relevant like
ForecastPOC - Decide if you want to use an existing VPC or not
- Change the Notebook Name (Optional)
- Alter the VolumeSize for the SageMaker EBS volume, default is 10GB, if your dataset is expected to be larger, please increase this accordingly.
When you are done click Next at the bottom.
This page is a bit longer so scroll to the bottom to click Next. All of the defaults should be sufficient to complete the POC, if you have custom requirements alter as necessary.
Again scroll to the bottom, check the box to enable the template to create new IAM resources and then click Create Stack.
For a few minutes CloudFormation will be creating the resources described above on your behalf it will look like this while it is provisioning:
Once it has completed you'll see green text like below indicating that the work has been completed:
Now that your environment has been created go to the service page for Sageamaker by clicking Services in the top of the console and then searching for SageMaker and clicking the service.
From the SageMaker console scroll until you see the green box indicating now many notebooks you have in service and click that.
On this page you will see a list of any SageMaker notebooks you have running, simply click the Open JupyterLab link on the Forecast POC notebook you have created
This will open the Jupyter environment for your POC, think of it as a web based data science IDE if you are not familiar with it. It should Automatically open the ForecastPOC folder for you, but if it does not do that by clicking on the folder icon in the browser on the left side of the screen and follow the documentation below to get started with your POC!
Open 0. Environment Setup.ipynb and follow along to set up Amazon S3 and AWS IAM resources required for file storage and permissions management in the cloud, ready for your Amazon Forecast project.
Open 1. Preparing Target Time-Series Data.ipynb and follow along to download a sample dataset and prepare a target time-series file (the quantity you want to forecast) and upload it to Amazon S3.
Follow through the process of creating your Amazon Forecast project, importing data, training your first models and evaluating their results - in either:
- The Amazon Forecast console, with 2a. Getting Started with Forecast (Console).ipynb, or
- The AWS SDK for Python, with 2b. Getting Started with Forecast (Python SDK).ipynb
Amazon Forecast can certainly generate predictions using only the target data, but the real power of the service comes into play when adding related time series information to facilitate better understanding of external signals, as well as item metadata that allows advanced models to make assumptions about how a time series may behave when missing chunks of information.
Open 3. Preparing Related Time-Series Data.ipynb and follow along there to prepare additional data features to use in Amazon Forecast.
Follow through the process of importing this newly prepared RTS data and creating and evaluating new forecasts, in either:
- The Amazon Forecast console, with 4a. Incorporating RTS Data (Console).ipynb, or
- The AWS SDK for Python, with 4b. Incorporating RTS Data (Python SDK).ipynb
After exploring Amazon Forecast with sample data, it's time to try producing models on your own real-world data; for which you can use these notebooks as a guide to get started.
The next step will be to compare the results from Forecast against your previous/current forecasting approach and determine which is more performant; considering carefully the potential benefits of using probabilistic forecasts generated by the Amazon Forecast service, rather than simple point-forecast methods.
As shown in the notebooks, data is exportable in JSON/CSV format so it's easy to develop automated procedures and integrations as you consider your path to production. Check out the official sample notebooks and the Amazon Forecast blog for more great resources to help get you started - such as this example on automating forecasting pipelines.
The scripts in this PoC provision various Forecast, S3, and IAM resources. If you ran this PoC in your own account and would like to avoid ongoing charges related to these resources, open Cleanup.ipynb and run the cleanup scripts provided there. Wait for the scripts to complete, then tear down the CloudFormation stack you created at the beginning of these instructions.