AWS Tools for Windows PowerShell
Command Reference

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Synopsis

Calls the Amazon Forecast Service CreatePredictor API operation.

Syntax

New-FRCPredictor
-PredictorName <String>
-AlgorithmArn <String>
-AutoMLOverrideStrategy <AutoMLOverrideStrategy>
-EvaluationParameters_BackTestWindowOffset <Int32>
-ParameterRanges_CategoricalParameterRange <CategoricalParameterRange[]>
-ParameterRanges_ContinuousParameterRange <ContinuousParameterRange[]>
-InputDataConfig_DatasetGroupArn <String>
-FeaturizationConfig_Featurization <Featurization[]>
-FeaturizationConfig_ForecastDimension <String[]>
-FeaturizationConfig_ForecastFrequency <String>
-ForecastHorizon <Int32>
-ForecastType <String[]>
-ParameterRanges_IntegerParameterRange <IntegerParameterRange[]>
-EncryptionConfig_KMSKeyArn <String>
-EvaluationParameters_NumberOfBacktestWindow <Int32>
-OptimizationMetric <OptimizationMetric>
-PerformAutoML <Boolean>
-PerformHPO <Boolean>
-EncryptionConfig_RoleArn <String>
-InputDataConfig_SupplementaryFeature <SupplementaryFeature[]>
-Tag <Tag[]>
-TrainingParameter <Hashtable>
-Select <String>
-PassThru <SwitchParameter>
-Force <SwitchParameter>

Description

This operation creates a legacy predictor that does not include all the predictor functionalities provided by Amazon Forecast. To create a predictor that is compatible with all aspects of Forecast, use CreateAutoPredictor. Creates an Amazon Forecast predictor. In the request, provide a dataset group and either specify an algorithm or let Amazon Forecast choose an algorithm for you using AutoML. If you specify an algorithm, you also can override algorithm-specific hyperparameters. Amazon Forecast uses the algorithm to train a predictor using the latest version of the datasets in the specified dataset group. You can then generate a forecast using the CreateForecast operation. To see the evaluation metrics, use the GetAccuracyMetrics operation. 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. By default, predictors are trained and evaluated at the 0.1 (P10), 0.5 (P50), and 0.9 (P90) quantiles. You can choose custom forecast types to train and evaluate your predictor by setting the ForecastTypes. 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 losses over the forecast types. By default, these are the 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. 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.

Parameters

-AlgorithmArn <String>
Amazon.ForecastService.Model.CreatePredictorRequest.AlgorithmArn
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-AutoMLOverrideStrategy <AutoMLOverrideStrategy>
The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.Used to overide the default AutoML strategy, which is to optimize predictor accuracy. To apply an AutoML strategy that minimizes training time, use LatencyOptimized.This parameter is only valid for predictors trained using AutoML.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-EncryptionConfig_KMSKeyArn <String>
The Amazon Resource Name (ARN) of the KMS key.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-EncryptionConfig_RoleArn <String>
The ARN of the IAM role that Amazon Forecast can assume to access the AWS KMS key.Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an InvalidInputException error.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-EvaluationParameters_BackTestWindowOffset <Int32>
The point from the end of the dataset where you want to split the data for model training and testing (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-EvaluationParameters_NumberOfBacktestWindow <Int32>
The number of times to split the input data. The default is 1. Valid values are 1 through 5.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesEvaluationParameters_NumberOfBacktestWindows
-FeaturizationConfig_Featurization <Featurization[]>
An array of featurization (transformation) information for the fields of a dataset.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesFeaturizationConfig_Featurizations
-FeaturizationConfig_ForecastDimension <String[]>
An array of dimension (field) names that specify how to group the generated forecast.For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesFeaturizationConfig_ForecastDimensions
-FeaturizationConfig_ForecastFrequency <String>
The frequency of predictions in a forecast.Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" indicates every year and "5min" indicates every five minutes.The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the RELATED_TIME_SERIES dataset frequency.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-Force <SwitchParameter>
This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-ForecastHorizon <Int32>
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.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-ForecastType <String[]>
Specifies the forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with mean. The default value is ["0.10", "0.50", "0.9"].
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesForecastTypes
-InputDataConfig_DatasetGroupArn <String>
The Amazon Resource Name (ARN) of the dataset group.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-InputDataConfig_SupplementaryFeature <SupplementaryFeature[]>
An array of supplementary features. The only supported feature is a holiday calendar.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesInputDataConfig_SupplementaryFeatures
-OptimizationMetric <OptimizationMetric>
The accuracy metric used to optimize the predictor.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-ParameterRanges_CategoricalParameterRange <CategoricalParameterRange[]>
Specifies the tunable range for each categorical hyperparameter.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHPOConfig_ParameterRanges_CategoricalParameterRanges
-ParameterRanges_ContinuousParameterRange <ContinuousParameterRange[]>
Specifies the tunable range for each continuous hyperparameter.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHPOConfig_ParameterRanges_ContinuousParameterRanges
-ParameterRanges_IntegerParameterRange <IntegerParameterRange[]>
Specifies the tunable range for each integer hyperparameter.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesHPOConfig_ParameterRanges_IntegerParameterRanges
-PassThru <SwitchParameter>
Changes the cmdlet behavior to return the value passed to the PredictorName parameter. The -PassThru parameter is deprecated, use -Select '^PredictorName' instead. This parameter will be removed in a future version.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-PerformAutoML <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.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-PerformHPO <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 algorithms support HPO:
  • DeepAR+
  • CNN-QR
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-PredictorName <String>
A name for the predictor.
Required?True
Position?1
Accept pipeline input?True (ByValue, ByPropertyName)
-Select <String>
Use the -Select parameter to control the cmdlet output. The default value is 'PredictorArn'. Specifying -Select '*' will result in the cmdlet returning the whole service response (Amazon.ForecastService.Model.CreatePredictorResponse). Specifying the name of a property of type Amazon.ForecastService.Model.CreatePredictorResponse will result in that property being returned. Specifying -Select '^ParameterName' will result in the cmdlet returning the selected cmdlet parameter value.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-Tag <Tag[]>
The optional metadata that you apply to the predictor to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.The following basic restrictions apply to tags:
  • Maximum number of tags per resource - 50.
  • For each resource, each tag key must be unique, and each tag key can have only one value.
  • Maximum key length - 128 Unicode characters in UTF-8.
  • Maximum value length - 256 Unicode characters in UTF-8.
  • If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
  • Tag keys and values are case sensitive.
  • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTags
-TrainingParameter <Hashtable>
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.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesTrainingParameters

Common Credential and Region Parameters

-AccessKey <String>
The AWS access key for the user account. This can be a temporary access key if the corresponding session token is supplied to the -SessionToken parameter.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAK
-Credential <AWSCredentials>
An AWSCredentials object instance containing access and secret key information, and optionally a token for session-based credentials.
Required?False
Position?Named
Accept pipeline input?True (ByValue, ByPropertyName)
-EndpointUrl <String>
The endpoint to make the call against.Note: This parameter is primarily for internal AWS use and is not required/should not be specified for normal usage. The cmdlets normally determine which endpoint to call based on the region specified to the -Region parameter or set as default in the shell (via Set-DefaultAWSRegion). Only specify this parameter if you must direct the call to a specific custom endpoint.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-NetworkCredential <PSCredential>
Used with SAML-based authentication when ProfileName references a SAML role profile. Contains the network credentials to be supplied during authentication with the configured identity provider's endpoint. This parameter is not required if the user's default network identity can or should be used during authentication.
Required?False
Position?Named
Accept pipeline input?True (ByValue, ByPropertyName)
-ProfileLocation <String>
Used to specify the name and location of the ini-format credential file (shared with the AWS CLI and other AWS SDKs)If this optional parameter is omitted this cmdlet will search the encrypted credential file used by the AWS SDK for .NET and AWS Toolkit for Visual Studio first. If the profile is not found then the cmdlet will search in the ini-format credential file at the default location: (user's home directory)\.aws\credentials.If this parameter is specified then this cmdlet will only search the ini-format credential file at the location given.As the current folder can vary in a shell or during script execution it is advised that you use specify a fully qualified path instead of a relative path.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAWSProfilesLocation, ProfilesLocation
-ProfileName <String>
The user-defined name of an AWS credentials or SAML-based role profile containing credential information. The profile is expected to be found in the secure credential file shared with the AWS SDK for .NET and AWS Toolkit for Visual Studio. You can also specify the name of a profile stored in the .ini-format credential file used with the AWS CLI and other AWS SDKs.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesStoredCredentials, AWSProfileName
-Region <Object>
The system name of an AWS region or an AWSRegion instance. This governs the endpoint that will be used when calling service operations. Note that the AWS resources referenced in a call are usually region-specific.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesRegionToCall
-SecretKey <String>
The AWS secret key for the user account. This can be a temporary secret key if the corresponding session token is supplied to the -SessionToken parameter.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesSK, SecretAccessKey
-SessionToken <String>
The session token if the access and secret keys are temporary session-based credentials.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesST

Outputs

System.String or Amazon.ForecastService.Model.CreatePredictorResponse
This cmdlet returns a System.String object. The service call response (type Amazon.ForecastService.Model.CreatePredictorResponse) can also be referenced from properties attached to the cmdlet entry in the $AWSHistory stack.
AWS Tools for PowerShell User Guide

Supported Version

AWS Tools for PowerShell: 2.x.y.z