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[ aws . forecast ]

create-predictor

Description

Creates an Amazon Forecast predictor.

In the request, you provide a dataset group and either specify an algorithm or let Amazon Forecast choose the algorithm for you using AutoML. If you specify an algorithm, you also can override algorithm-specific hyperparameters.

Amazon Forecast uses the chosen algorithm to train a model using the latest version of the datasets in the specified dataset group. The result is called a predictor. You then generate a forecast using the CreateForecast operation.

After training a model, the CreatePredictor operation also evaluates it. To see the evaluation metrics, use the GetAccuracyMetrics operation. Always review the evaluation metrics before deciding to use the predictor to generate a forecast.

Optionally, you can specify a featurization configuration to fill and aggregate the data fields in the TARGET_TIME_SERIES dataset to improve model training. For more information, see FeaturizationConfig .

For RELATED_TIME_SERIES datasets, CreatePredictor verifies that the DataFrequency specified when the dataset was created matches the ForecastFrequency . TARGET_TIME_SERIES datasets don't have this restriction. Amazon Forecast also verifies the delimiter and timestamp format. For more information, see howitworks-datasets-groups .

AutoML

If you want Amazon Forecast to evaluate each algorithm and choose the one that minimizes the objective function , set PerformAutoML to true . The objective function is defined as the mean of the weighted p10, p50, and p90 quantile losses. For more information, see EvaluationResult .

When AutoML is enabled, the following properties are disallowed:

  • AlgorithmArn
  • HPOConfig
  • PerformHPO
  • TrainingParameters

To get a list of all of your predictors, use the ListPredictors operation.

Note

Before you can use the predictor to create a forecast, the Status of the predictor must be ACTIVE , signifying that training has completed. To get the status, use the DescribePredictor operation.

See also: AWS API Documentation

See 'aws help' for descriptions of global parameters.

Synopsis

  create-predictor
--predictor-name <value>
[--algorithm-arn <value>]
--forecast-horizon <value>
[--perform-auto-ml | --no-perform-auto-ml]
[--perform-hpo | --no-perform-hpo]
[--training-parameters <value>]
[--evaluation-parameters <value>]
[--hpo-config <value>]
--input-data-config <value>
--featurization-config <value>
[--encryption-config <value>]
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]

Options

--predictor-name (string)

A name for the predictor.

--algorithm-arn (string)

The Amazon Resource Name (ARN) of the algorithm to use for model training. Required if PerformAutoML is not set to true .

Supported algorithms:
  • arn:aws:forecast:::algorithm/ARIMA
  • arn:aws:forecast:::algorithm/Deep_AR_Plus Supports hyperparameter optimization (HPO)
  • arn:aws:forecast:::algorithm/ETS
  • arn:aws:forecast:::algorithm/NPTS
  • arn:aws:forecast:::algorithm/Prophet

--forecast-horizon (integer)

Specifies the number of time-steps that the model is trained to predict. The forecast horizon is also called the prediction length.

For example, if you configure a dataset for daily data collection (using the DataFrequency parameter of the CreateDataset operation) and set the forecast horizon to 10, the model returns predictions for 10 days.

The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.

--perform-auto-ml | --no-perform-auto-ml (boolean)

Whether to perform AutoML. When Amazon Forecast performs AutoML, it evaluates the algorithms it provides and chooses the best algorithm and configuration for your training dataset.

The default value is false . In this case, you are required to specify an algorithm.

Set PerformAutoML to true to have Amazon Forecast perform AutoML. This is a good option if you aren't sure which algorithm is suitable for your training data. In this case, PerformHPO must be false.

--perform-hpo | --no-perform-hpo (boolean)

Whether to perform hyperparameter optimization (HPO). HPO finds optimal hyperparameter values for your training data. The process of performing HPO is known as running a hyperparameter tuning job.

The default value is false . In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.

To override the default values, set PerformHPO to true and, optionally, supply the HyperParameterTuningJobConfig object. The tuning job specifies a metric to optimize, which hyperparameters participate in tuning, and the valid range for each tunable hyperparameter. In this case, you are required to specify an algorithm and PerformAutoML must be false.

The following algorithm supports HPO:

  • DeepAR+

--training-parameters (map)

The hyperparameters to override for model training. The hyperparameters that you can override are listed in the individual algorithms. For the list of supported algorithms, see aws-forecast-choosing-recipes .

Shorthand Syntax:

KeyName1=string,KeyName2=string

JSON Syntax:

{"string": "string"
  ...}

--evaluation-parameters (structure)

Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.

Shorthand Syntax:

NumberOfBacktestWindows=integer,BackTestWindowOffset=integer

JSON Syntax:

{
  "NumberOfBacktestWindows": integer,
  "BackTestWindowOffset": integer
}

--hpo-config (structure)

Provides hyperparameter override values for the algorithm. If you don't provide this parameter, Amazon Forecast uses default values. The individual algorithms specify which hyperparameters support hyperparameter optimization (HPO). For more information, see aws-forecast-choosing-recipes .

If you included the HPOConfig object, you must set PerformHPO to true.

JSON Syntax:

{
  "ParameterRanges": {
    "CategoricalParameterRanges": [
      {
        "Name": "string",
        "Values": ["string", ...]
      }
      ...
    ],
    "ContinuousParameterRanges": [
      {
        "Name": "string",
        "MaxValue": double,
        "MinValue": double,
        "ScalingType": "Auto"|"Linear"|"Logarithmic"|"ReverseLogarithmic"
      }
      ...
    ],
    "IntegerParameterRanges": [
      {
        "Name": "string",
        "MaxValue": integer,
        "MinValue": integer,
        "ScalingType": "Auto"|"Linear"|"Logarithmic"|"ReverseLogarithmic"
      }
      ...
    ]
  }
}

--input-data-config (structure)

Describes the dataset group that contains the data to use to train the predictor.

Shorthand Syntax:

DatasetGroupArn=string,SupplementaryFeatures=[{Name=string,Value=string},{Name=string,Value=string}]

JSON Syntax:

{
  "DatasetGroupArn": "string",
  "SupplementaryFeatures": [
    {
      "Name": "string",
      "Value": "string"
    }
    ...
  ]
}

--featurization-config (structure)

The featurization configuration.

JSON Syntax:

{
  "ForecastFrequency": "string",
  "ForecastDimensions": ["string", ...],
  "Featurizations": [
    {
      "AttributeName": "string",
      "FeaturizationPipeline": [
        {
          "FeaturizationMethodName": "filling",
          "FeaturizationMethodParameters": {"string": "string"
            ...}
        }
        ...
      ]
    }
    ...
  ]
}

--encryption-config (structure)

An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

Shorthand Syntax:

RoleArn=string,KMSKeyArn=string

JSON Syntax:

{
  "RoleArn": "string",
  "KMSKeyArn": "string"
}

--cli-input-json (string) Performs service operation based on the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, the CLI values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See 'aws help' for descriptions of global parameters.

Output

PredictorArn -> (string)

The Amazon Resource Name (ARN) of the predictor.