Updating Data - Amazon Forecast
Import modesUpdating existing datasetsUpdating forecasts

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Updating Data

As you collect new data, you will want to import that data into Forecast. To do so, you have two options, replacement and incremental updates. A replacement dataset import job will overwrite all existing data with the newly imported data. An incremental update will append the newly imported data to the dataset.

After importing your new data, you can use an existing predictor to generate a forecast for that data.

Import modes

To configure how Amazon Forecast adds new data to existing dataset, you specify the import mode for your dataset import job. The default import mode is FULL. You can only configure the import mode by using the Amazon Forecast API.

  • To overwrite all existing data in your dataset, specify FULL in the CreateDatasetImportJob API operation.

  • To append the records to the existing data in your dataset, specify INCREMENTAL in the CreateDatasetImportJob API operation. If an existing record and an imported record have the same timeseries ID (item ID, dimension, and timestamp), then the existing record is replaced with the newly imported record. Amazon Forecast always uses the record with the most recent timestamp.

If you have not imported a dataset, the incremental option is not available. The default import mode is a full replacement.

Incremental import mode guidelines

When you perform an incremental dataset import, you cannot change the timestamp format, data format, or geolocation data. To change any of these items, you need to perform a full data dataset import.

Updating existing datasets

Important

By default, a dataset import job replaces any existing data in the dataset that you imported into. You can change this by specifying the dataset import job's Import modes.

To update a dataset, create a dataset import job for the dataset and specify the import mode.

CLI

To update a dataset, use the create-dataset-import-job command. For the import-mode, specify FULL, to replace existing data or INCREMENTAL to add to it. For more information, see Import modes.

The following code shows how to create a dataset import job that incrementally imports new data into a dataset.

aws forecast create-dataset-import-job \
                        --dataset-import-job-name dataset import job name \
                        --dataset-arn dataset arn \
                        --data-source "S3Config":{"KMSKeyArn":"string", "Path":"string", "RoleArn":"string"} \
                        --import-mode INCREMENTAL
Python

To update a dataset, use the create_dataset_import_job method. For the import-mode, specify FULL, to replace existing data or INCREMENTAL to add to it. For more information, see Import modes.

import boto3

forecast = boto3.client('forecast')

response = forecast.create_dataset_import_job(
    datasetImportJobName = 'YourImportJob',
    datasetArn = 'dataset_arn',
    dataSource = {"S3Config":{"KMSKeyArn":"string", "Path":"string", "RoleArn":"string"}},
    importMode = 'INCREMENTAL'
)

Updating forecasts

As you collect new data, you might want to use it to generate new forecasts. Forecast does not automatically retrain a predictor when you import an updated dataset, but you can manually retrain a predictor to generate a new forecast with the updated data. For instance, if you collect daily sales data and want to include new data points in your forecast, you could import the updated data and use it to generate a forecast without training a new predictor. For newly imported data to have an impact on your forecasts, you must retrain the predictor.

To generate a forecast from the new data:

  1. Upload the new data to an Amazon S3 bucket. Your new data should contain only the data added since your last data set import.

  2. Create an Incremental dataset import job with the new data. The new data is appended to the existing data and the forecast is generated from the updated data. If your new data file contains both previously-imported data and new data, create a Full dataset import job.

  3. Create a new forecast using the existing predictor.

  4. Retrieve the forecast as usual.