Generating Forecasts - Amazon Forecast

Generating Forecasts

After you create an Amazon Forecast predictor, you are ready to create a forecast. By default, a forecast includes predictions for every item (item_id) in the dataset group that was used to train the predictor. However, you can specify a subset of items that are used to generate a forecast.

After you create a forecast, you can export it to your Amazon Simple Storage Service (Amazon S3) bucket.

Creating a forecast

You can create a forecast with the Forecast console, AWS CLI, or AWS SDKs. The status of your predictor must be Active before you can generate a forecast.

Console

To create a forecast

  1. Sign in to the AWS Management Console and open the Amazon Forecast console at https://console.aws.amazon.com/forecast/.

  2. From Dataset groups, choose your dataset group.

  3. On your dataset group's Dashboard, under Generate forecasts, choose Create a forecast. The Create a forecast page appears.

  4. On the Create a forecast page, for Forecast details, provide a name for your forecast and choose the predictor you want to use to create forecasts.

  5. For Forecast quantiles, optionally specify the quantiles at which probabilistic forecasts are generated. The default quantiles are the quantiles you specified during predictor creation.

  6. Optionally, choose the radio button for Selected Items to specify a subset of time series that are used for forecast generation.

  7. Optionally, add any tags for the forecast. For more information see Tagging Amazon Forecast Resources.

  8. Choose Start. The Forecasts page appears.

    The Status column lists the status of your forecast. Wait for Amazon Forecast to finish creating the forecast. The process can take several minutes or longer. When your forecast has been created, the status transitions to Active.

    Now that your forecast has been created, you can export the forecast. See Exporting a forecast.

CLI

To create a forecast with the AWS CLI, use the create-forecast command. Provide a name for the forecast and the Amazon Resource Name (ARN) of your predictor. For forecast-types, optionally specify the quantiles at which probabilistic forecasts are generated. The default values are the quantiles you specified when you created the predictor. Optionally add any tags for the forecast. For more information see Tagging Amazon Forecast Resources.

For information on required and optional parameters see CreateForecast.

aws forecast create-forecast \ --forecast-name forecast_name \ --forecast-types 0.1 0.5 0.9 \ --predictor-arn arn:aws:forecast:region:account_number:predictor/predictorName \ --tags Key=key1,Value=value1 Key=key2,Value=value2
Python

To create a forecast with the SDK for Python (Boto3), use the create_forecast method. Provide a name for the forecast and the Amazon Resource Name (ARN) of your predictor. For ForecastTypes, optionally specify the quantiles at which probabilistic forecasts are generated. The default values are the quantiles you specified when you created the predictor. Optionally add any tags for the forecast. For more information see Tagging Amazon Forecast Resources.

For information on required and optional parameters see CreateForecast.

import boto3 forecast = boto3.client('forecast') create_forecast_response = forecast.create_forecast( ForecastName = "Forecast_Name", ForecastTypes = ["0.1", "0.5", "0.9"], # optional, the default types/quantiles are what you specified for the predictor PredictorArn = "arn:aws:forecast:region:accountNumber:predictor/predictorName", Tags = [ { "Key": "key1", "Value": "value1" }, { "Key": "key2", "Value": "value2" } ] ) forecast_arn = create_forecast_response['ForecastArn'] print(forecast_arn)

Specifying time series

Note

A time series is a combination of the item (item_id) and all dimensions in your datasets.

To specify a list of time series, upload a CSV file identifying the time series by their item_id and dimension values to an S3 bucket. You must also define the attributes and attribute types of the time series in a schema.

For example, a retailer may want to know how an advertising campaign impacts sales for a specific item (item_id) at a specific store location (store_location). In this use case, you would specify the time series that is the combination of item_id and store_location.

The following CSV file selects the following five time series:

  1. Item_id: 001, store_location: Seattle

  2. Item_id: 001, store_location: New York

  3. Item_id: 002, store_location: Seattle

  4. Item_id: 002, store_location: New York

  5. Item_id: 003, store_location: Denver

001, Seattle 001, New York 002, Seattle 002, New York 003, Denver

The schema defines the first column as item_id and the second column as store_location.

Forecast creation is skipped for any time series that you specify that are not in the input dataset. The forecast export file will not contain these time series or their forecasted values.

Exporting a forecast

After you create a forecast, you can export it to an Amazon S3 bucket. Exporting a forecast copies the forecast to your Amazon S3 bucket as a CSV file (by default), and the exported data includes all attributes of any item metadata dataset in addition to item predictions. You can specify the Parquet file format when you export a forecast.

The granularity of the exported forecasts (such as hourly, daily, or weekly) is the forecast frequency that you specified when you created the predictor. You can optionally specify an AWS Key Management Service key to encrypt the data before it's written to the bucket.

Note

Export files can directly return information from the Dataset Import. This makes the files vulnerable to CSV injection if the imported data contains formulas or commands. For this reason, exported files can prompt security warnings. To avoid malicious activity, disable links and macros when reading exported files.

Console

To export a forecast

  1. In the navigation pane, under your dataset group, choose Forecasts.

  2. Choose the radio button for your forecast and choose Create forecast export. The Create forecast export page is displayed.

  3. On the Create forecast export page, for Export details, provide the following information.

    • Export name – Enter a name for your forecast export job.

    • Generated forecast – From the drop-down menu, choose the forecast that you created in Step 3: Create a Forecast.

    • IAM role – Either keep the default Enter a custom IAM role ARN or choose Create a new role to have Amazon Forecast create the role for you.

    • Custom IAM role ARN – If you are entering a custom IAM role, enter the Amazon Resource Name (ARN) of the IAM role that you created in Create an IAM Role for Amazon Forecast (IAM Console).

    • KMS key ARN – If you use AWS Key Management Service for bucket encryption, provide the Amazon Resource Name (ARN) of the AWS KMS key.

    • S3 forecast export location – Use the following format to enter the location of your Amazon Simple Storage Service (Amazon S3) bucket or folder in the bucket:

      s3://<name of your S3 bucket>/<folder path>/

  4. Choose Create forecast export. The my_forecast page is displayed.

    Wait for Amazon Forecast to finish exporting the forecast. The process can take several minutes or longer. When your forecast has been exported, the status transitions to Active and you can find the forecast files in your Amazon S3 bucket.

CLI

To export a forecast with the AWS CLI you use the export-forecast-job command. Give the forecast export job a name, specify the ARN of the forecast to export, and optionally add any tags. For the destination, specify the path to your output Amazon S3 bucket, the ARN of the IAM role that you created in Create an IAM Role for Amazon Forecast (IAM Console), and if you use a AWS KMS key for bucket encryption, the ARN for your key.

For more information about required and optional parameters, see CreateForecastExportJob operation.

forecast create-forecast-export-job \ --forecast-export-job-name exportJobName \ --forecast-arn arn:aws:forecast:region:acctNumber:forecast/forecastName \ --destination S3Config="{Path='s3://bucket/folderName',RoleArn='arn:aws:iam::acctNumber:role/Role, KMSKeyArn='arn:aws:kms:region:accountNumber:key/keyID'}" --tags Key=key1,Value=value1 Key=key2,Value=value2
Python

To export a forecast with the SDK for Python (Boto3) you use the export_forecast_job method. Give the forecast export job a name, specify the ARN of the forecast to export, and optionally add any tags. For the Destination, specify the path to your output Amazon S3 bucket, the ARN of the IAM role that you created in Create an IAM Role for Amazon Forecast (IAM Console), and if you use a AWS KMS key for bucket encryption, the ARN for your key.

For more information about required and optional parameters, see CreateForecastExportJob operation.

import boto3 forecast = boto3.client('forecast') export_forecast_response = forecast.create_forecast_export_job( Destination = { "S3Config": { "Path": "s3://bucketName/folderName/", "RoleArn": "arn:aws:iam::accountNumber:role/roleName", "KMSKeyArn": "arn:aws:kms:region:accountNumber:key/keyID" } }, ForecastArn = "arn:aws:forecast:region:accountNumber:forecast/forecastName", ForecastExportJobName = "export_job_name", Tags = [ { "Key": "key1", "Value": "value1" }, { "Key": "key2", "Value": "value2" } ] ) forecast_export_job_arn = export_forecast_response["ForecastExportJobArn"] print(forecast_export_job_arn)

Querying a forecast

You can query a forecast using the QueryForecast operation. By default, the complete range of the forecast is returned. You can request a specific date range within the complete forecast.

When you query a forecast you must specify filtering criteria. A filter is a key-value pair. The key is one of the schema attribute names (including forecast dimensions) from one of the datasets used to create the forecast. The value is a valid values for the specified key. You can specify multiple key-value pairs. The returned forecast will only contain items that satisfy all the criteria.