Training Predictors

A predictor is an Amazon Forecast model that is trained using your target time series, related time series, item metadata, and any additional datasets you include. You can use predictors to generate forecasts based on your time-series data.

By default, Amazon Forecast creates an AutoPredictor, where Forecast applies the optimal combination of algorithms to each time series in your datasets.

Topics

• Creating a Predictor
• Upgrading to AutoPredictor
• Data aggregation for different forecast frequencies
• Using additional datasets
• Working with legacy predictors
• Evaluating Predictor Accuracy
• Retraining Predictors
• Weather Index
• Holidays Featurization
• Predictor Explainability
• Predictor Monitoring
• Amazon Forecast Algorithms

Creating a Predictor

Amazon Forecast requires the following inputs to train a predictor:

• Dataset group – A dataset group that must include a target time series dataset. The target time series dataset includes the target attribute (item_id) and timestamp attribute, as well as any dimensions. Related time series and Item metadata is optional. For more information, see Importing Datasets.

• Forecast frequency – The granularity of your forecasts (hourly, daily, weekly, etc). Amazon Forecast lets you determine the exact granularity of your forecasts when you provide the frequency unit and value. Only integer values are allowed

  Frequency unit	Allowed values
  Minutely	1-59
  Hourly	1-23
  Daily	1-6
  Weekly	1-4
  Monthly	1-11
  Yearly	1

  For example, if you want every other week forecasts, your frequency unit is weekly and the value is 2. Or, if you want quarterly forecasts, your frequency unit is monthly and the value is 3.

  When your data is collected at a greater frequency than the forecast frequency, it is aggregated to the forecast frequency. This includes the trailing time series and related time series data. For more information on aggregation see Data aggregation for different forecast frequencies.

• Forecast horizon – The number of time steps being forecasted.

You can also set values for the following optional inputs:

• Time alignment boundary – The time boundary Forecast uses to aggregate your data and generate forecasts that align with the forecast frequency you specify. For more information on aggregation see Data aggregation for different forecast frequencies. For information on specifying a time boundary see Time Boundaries.

• Forecast dimensions – Dimensions are optional attributes in your target time series dataset that can be used in combination with the target value (item_id) to create separate time series.

• Forecast types – The quantiles used to evaluate your predictor.

• Optimization metric – The accuracy metric used to optimize your predictor.

• Additional datasets – Built-in Amazon Forecast datasets like the Weather Index and Holidays.

You can create a predictor using the Software Development Kit (SDK) or the Amazon Forecast console.

Console

To create a predictor

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. In the navigation pane, choose Predictors.

4. Choose Train new predictor.

5. Provide values for the following mandatory fields:

   • Name - a unique predictor name.

   • Forecast frequency - the granularity of your forecasts.

   • Forecast horizon - The number of time steps to forecast.

6. Choose Start.

For information on additional datasets, see Weather Index and Holidays Featurization. To learn more about customizing forecast types and optimization metrics, see Evaluating Predictor Accuracy.

AWS CLI

To create an auto predictor with the AWS CLI, use the create-predictor command. The following code creates an auto predictor that makes predictions for 14 days in the future.

Provide a name for the predictor and the Amazon Resource Name (ARN) of the dataset group that includes your training data. Optionally modify the forecast horizon and forecast frequency. Optionally add any tags for the predictor. For more information see Tagging Amazon Forecast Resources.

For information on required and optional parameters see CreateAutoPredictor.

aws forecast create-predictor \
--predictor-name predictor_name \
--data-config DatasetGroupArn="arn:aws:forecast:region:account:dataset-group/datasetGroupName" \
--forecast-horizon 14 \
--forecast-frequency D \
--tags Key=key1,Value=value1 Key=key2,Value=value2

To learn more about customizing forecast types and optimization metrics, see Evaluating Predictor Accuracy The Weather Index and Holidays additional datasets are defined within the DataConfig datatype. For information on additional datasets, see Weather Index and Holidays Featurization.

Python

To create an auto predictor with the SDK for Python (Boto3), use the create_auto_predictor method. The following code creates an auto predictor that makes predictions for 14 days in the future.

Provide a name for the predictor and the Amazon Resource Name (ARN) of the dataset group that includes your training data. Optionally modify the forecast horizon and forecast frequency. Optionally add any tags for the predictor. For more information see Tagging Amazon Forecast Resources.

For information on required and optional parameters see CreateAutoPredictor.

import boto3
            
forecast = boto3.client('forecast')

create_predictor_response = forecast.create_auto_predictor(
    PredictorName = 'predictor_name',
    ForecastHorizon = 14,
    ForecastFrequency = 'D',
    DataConfig = {
      "DatasetGroupArn": "arn:aws:forecast:region:account:dataset-group/datasetGroupName"
    },
    Tags = [ 
      { 
         "Key": "key1",
         "Value": "value1"
      },
      { 
         "Key": "key2",
         "Value": "value2"
      }
    ]
)
print(create_predictor_response['PredictorArn'])

To learn more about customizing forecast types and optimization metrics, see Evaluating Predictor Accuracy The Weather Index and Holidays additional datasets are defined within the DataConfig datatype. For information on additional datasets, see Weather Index and Holidays Featurization.

Upgrading to AutoPredictor

Python notebooks

For a step-by-step guide on upgrading predictors to AutoPredictor, see Upgrading a predictor to AutoPredictor.

Predictors created with AutoML or manual selection (CreatePredictor) can be upgraded to an AutoPredictor. Upgrading an existing to AutoPredictor will transfer all the relevant predictor configuration settings.

After Upgrading to AutoPredictor, the original predictor will remain active and the upgraded predictor will have a separate Predictor ARN. This enables you to compare accuracy metrics between the two predictors, and you can still generate forecasts with the original predictor.

You can upgrade a predictor using the Software Development Kit (SDK) or the Amazon Forecast console.

Console

To upgrade a predictor

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

2. In the navigation pane, choose Predictors.

3. Choose the predictor to upgrade, and choose Upgrade.

4. Set a unique name for the upgraded predictor.

5. Choose Upgrade to AutoPredictor.

CLI

To upgrade a predictor with the AWS CLI, use the create-predictor method, but specify only the predictor name and the value of reference-predictor-arn (the ARN of the predictor you want to upgrade).

aws forecast create-predictor \
--predictor-name predictor_name \
--reference-predictor-arn arn:aws:forecast:region:account:predictor/predictorName

Python

To upgrade a predictor with the SDK for Python (Boto3), use the create_auto_predictor method, but specify only the predictor name and the value of ReferencePredictorArn (the ARN of the predictor you want to upgrade).

import boto3
            
forecast = boto3.client('forecast')

create_predictor_response = forecast.create_auto_predictor(
    PredictorName = 'predictor_name',
    ReferencePredictorArn = 'arn:aws:forecast:region:account:predictor/predictorName'
)
print(create_predictor_response['PredictorArn'])

Using additional datasets

Amazon Forecast can include the Weather Index and Holidays when creating your predictor. The Weather Index incorporates meteorological information into your model and Holidays incorporates information regarding national holidays.

The Weather Index requires a ‘geolocation’ attribute in your target time series dataset and information regarding time zones for your timestamps. For more information, see Weather Index.

Holidays includes holiday information on over 250 countries. For more information, see Holidays Featurization.

Working with legacy predictors

Note

To upgrade an existing predictor to AutoPredictor, see Upgrading to AutoPredictor

AutoPredictor is the default and preferred method to create a predictor with Amazon Forecast. AutoPredictor creates predictors by applying the optimal combination of algorithms for each time series in your dataset.

Predictors created with AutoPredictor are generally more accurate than predictors created with AutoML or manual selection. The Forecast Explainability and predictor retraining features are only available for predictors created with AutoPredictor.

Amazon Forecast can also create legacy predictors in the following ways:

1. AutoML - Forecast finds the best-performing algorithm and applies it to your entire dataset.

2. Manual selection - Manually choose a single algorithm that is applied to your entire dataset.

You might be able to create a legacy predictor using the Software Development Kit (SDK).

SDK

To use AutoML

Using the CreatePredictor operation, set the value of PerformAutoML to "true".

{
    ...
    "PerformAutoML": "true",
}

If you use AutoML, you cannot set a value for the following CreatePredictor parameters: AlgorithmArn, HPOConfig, TrainingParameters.