describe_auto_predictor(**kwargs)¶Describes a predictor created using the CreateAutoPredictor operation.
See also: AWS API Documentation
Request Syntax
response = client.describe_auto_predictor(
PredictorArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the predictor.
{
'PredictorArn': 'string',
'PredictorName': 'string',
'ForecastHorizon': 123,
'ForecastTypes': [
'string',
],
'ForecastFrequency': 'string',
'ForecastDimensions': [
'string',
],
'DatasetImportJobArns': [
'string',
],
'DataConfig': {
'DatasetGroupArn': 'string',
'AttributeConfigs': [
{
'AttributeName': 'string',
'Transformations': {
'string': 'string'
}
},
],
'AdditionalDatasets': [
{
'Name': 'string',
'Configuration': {
'string': [
'string',
]
}
},
]
},
'EncryptionConfig': {
'RoleArn': 'string',
'KMSKeyArn': 'string'
},
'ReferencePredictorSummary': {
'Arn': 'string',
'State': 'Active'|'Deleted'
},
'EstimatedTimeRemainingInMinutes': 123,
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1),
'OptimizationMetric': 'WAPE'|'RMSE'|'AverageWeightedQuantileLoss'|'MASE'|'MAPE',
'ExplainabilityInfo': {
'ExplainabilityArn': 'string',
'Status': 'string'
},
'MonitorInfo': {
'MonitorArn': 'string',
'Status': 'string'
},
'TimeAlignmentBoundary': {
'Month': 'JANUARY'|'FEBRUARY'|'MARCH'|'APRIL'|'MAY'|'JUNE'|'JULY'|'AUGUST'|'SEPTEMBER'|'OCTOBER'|'NOVEMBER'|'DECEMBER',
'DayOfMonth': 123,
'DayOfWeek': 'MONDAY'|'TUESDAY'|'WEDNESDAY'|'THURSDAY'|'FRIDAY'|'SATURDAY'|'SUNDAY',
'Hour': 123
}
}
Response Structure
The Amazon Resource Name (ARN) of the predictor
The name of the predictor.
The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.
The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"].
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.
An array of dimension (field) names that specify the attributes used to group your time series.
An array of the ARNs of the dataset import jobs used to import training data for the predictor.
The data configuration for your dataset group and any additional datasets.
The ARN of the dataset group used to train the predictor.
Aggregation and filling options for attributes in your dataset group.
Provides information about the method used to transform attributes.
The following is an example using the RETAIL domain:
{
"AttributeName": "demand",
"Transformations": {"aggregation": "sum", "middlefill": "zero", "backfill": "zero"}
}
The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target time series and the related time series datasets. For example, for the RETAIL domain, the target is demand .
The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.
The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Default values are bolded.
aggregation : sum , avg , first , min , maxfrontfill : nonemiddlefill : zero , nan (not a number), value , median , mean , min , maxbackfill : zero , nan , value , median , mean , min , maxThe following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):
middlefill : zero , value , median , mean , min , maxbackfill : zero , value , median , mean , min , maxfuturefill : zero , value , median , mean , min , maxTo set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2" .
Additional built-in datasets like Holidays and the Weather Index.
Describes an additional dataset. This object is part of the DataConfig object. Forecast supports the Weather Index and Holidays additional datasets.
Weather Index
The Amazon Forecast Weather Index is a built-in dataset that incorporates historical and projected weather information into your model. The Weather Index supplements your datasets with over two years of historical weather data and up to 14 days of projected weather data. For more information, see Amazon Forecast Weather Index.
Holidays
Holidays is a built-in dataset that incorporates national holiday information into your model. It provides native support for the holiday calendars of 66 countries. To view the holiday calendars, refer to the Jollyday library. For more information, see Holidays Featurization.
The name of the additional dataset. Valid names: "holiday" and "weather" .
Weather Index
To enable the Weather Index, do not specify a value for Configuration .
HolidaysHolidays
To enable Holidays, set CountryCode to one of the following two-letter country codes:
An Key Management Service (KMS) key and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key. You can specify this optional object in the CreateDataset and CreatePredictor requests.
The ARN of the IAM role that Amazon Forecast can assume to access the KMS key.
Passing a role across Amazon Web Services accounts is not allowed. If you pass a role that isn't in your account, you get an InvalidInputException error.
The Amazon Resource Name (ARN) of the KMS key.
The ARN and state of the reference predictor. This parameter is only valid for retrained or upgraded predictors.
The ARN of the reference predictor.
Whether the reference predictor is Active or Deleted .
The estimated time remaining in minutes for the predictor training job to complete.
The status of the predictor. States include:
ACTIVECREATE_PENDING , CREATE_IN_PROGRESS , CREATE_FAILEDCREATE_STOPPING , CREATE_STOPPEDDELETE_PENDING , DELETE_IN_PROGRESS , DELETE_FAILEDIn the event of an error, a message detailing the cause of the error.
The timestamp of the CreateAutoPredictor request.
The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime .CREATE_IN_PROGRESS - The current timestamp.CREATE_STOPPING - The current timestamp.CREATE_STOPPED - When the job stopped.ACTIVE or CREATE_FAILED - When the job finished or failed.The accuracy metric used to optimize the predictor.
Provides the status and ARN of the Predictor Explainability.
The Amazon Resource Name (ARN) of the Explainability.
The status of the Explainability. States include:
ACTIVECREATE_PENDING , CREATE_IN_PROGRESS , CREATE_FAILEDCREATE_STOPPING , CREATE_STOPPEDDELETE_PENDING , DELETE_IN_PROGRESS , DELETE_FAILEDA object with the Amazon Resource Name (ARN) and status of the monitor resource.
The Amazon Resource Name (ARN) of the monitor resource.
The status of the monitor. States include:
ACTIVEACTIVE_STOPPING , ACTIVE_STOPPEDUPDATE_IN_PROGRESSCREATE_PENDING , CREATE_IN_PROGRESS , CREATE_FAILEDDELETE_PENDING , DELETE_IN_PROGRESS , DELETE_FAILEDThe time boundary Forecast uses when aggregating data.
The month to use for time alignment during aggregation. The month must be in uppercase.
The day of the month to use for time alignment during aggregation.
The day of week to use for time alignment during aggregation. The day must be in uppercase.
The hour of day to use for time alignment during aggregation.
Exceptions
ForecastService.Client.exceptions.InvalidInputExceptionForecastService.Client.exceptions.ResourceNotFoundException