auto_predictors

Creates, updates, deletes, gets or lists an auto_predictors resource.

Overview

Name	auto_predictors
Type	Resource
Id	aws.forecast.auto_predictors

Fields

The following fields are returned by SELECT queries:

describe_auto_predictor

Name	Datatype	Description
creation_time	string (date-time)	The timestamp of the CreateAutoPredictor request.
data_config	object	The data configuration for your dataset group and any additional datasets.
dataset_import_job_arns	array	An array of the ARNs of the dataset import jobs used to import training data for the predictor.
encryption_config	object	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.
estimated_time_remaining_in_minutes	integer (int64)	The estimated time remaining in minutes for the predictor training job to complete.
explainability_info	object	Provides the status and ARN of the Predictor Explainability.
forecast_dimensions	array	An array of dimension (field) names that specify the attributes used to group your time series.
forecast_frequency	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. (pattern: <code>^Y|M|W|D|H|30min|15min|10min|5min|1min$</code>)
forecast_horizon	integer	The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.
forecast_types	array	The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"].
last_modification_time	string (date-time)	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.
message	string	In the event of an error, a message detailing the cause of the error.
monitor_info	object	A object with the Amazon Resource Name (ARN) and status of the monitor resource.
optimization_metric	string	The accuracy metric used to optimize the predictor. (WAPE, RMSE, AverageWeightedQuantileLoss, MASE, MAPE)
predictor_arn	string	The Amazon Resource Name (ARN) of the predictor (pattern: <code>arn:([a-z\d-]+):forecast:.:.:.+</code>)
predictor_name	string	The name of the predictor. (pattern: <code>^[a-zA-Z][a-zA-Z0-9_]*</code>)
reference_predictor_summary	object	The ARN and state of the reference predictor. This parameter is only valid for retrained or upgraded predictors.
status	string	The status of the predictor. States include: ACTIVE CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED CREATE_STOPPING, CREATE_STOPPED DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
time_alignment_boundary	object	The time boundary Forecast uses when aggregating data.

Methods

The following methods are available for this resource:

Name	Accessible by	Required Params	Optional Params	Description
describe_auto_predictor	select	region		Describes a predictor created using the CreateAutoPredictor operation.
create_auto_predictor	insert	region, PredictorName		Creates an Amazon Forecast predictor. Amazon Forecast creates predictors with AutoPredictor, which involves applying the optimal combination of algorithms to each time series in your datasets. You can use CreateAutoPredictor to create new predictors or upgrade/retrain existing predictors. Creating new predictors The following parameters are required when creating a new predictor: PredictorName - A unique name for the predictor. DatasetGroupArn - The ARN of the dataset group used to train the predictor. ForecastFrequency - The granularity of your forecasts (hourly, daily, weekly, etc). ForecastHorizon - The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. When creating a new predictor, do not specify a value for ReferencePredictorArn. Upgrading and retraining predictors The following parameters are required when retraining or upgrading a predictor: PredictorName - A unique name for the predictor. ReferencePredictorArn - The ARN of the predictor to retrain or upgrade. When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and PredictorName.

Parameters

Parameters can be passed in the WHERE clause of a query. Check the Methods section to see which parameters are required or optional for each operation.

Name	Datatype	Description
region	string	AWS region (default: us-east-1)

SELECT examples

describe_auto_predictor

Describes a predictor created using the CreateAutoPredictor operation.

SELECT
creation_time,
data_config,
dataset_import_job_arns,
encryption_config,
estimated_time_remaining_in_minutes,
explainability_info,
forecast_dimensions,
forecast_frequency,
forecast_horizon,
forecast_types,
last_modification_time,
message,
monitor_info,
optimization_metric,
predictor_arn,
predictor_name,
reference_predictor_summary,
status,
time_alignment_boundary
FROM aws.forecast.auto_predictors
WHERE region = '{{ region }}' -- required
;

INSERT examples

create_auto_predictor

Creates an Amazon Forecast predictor. Amazon Forecast creates predictors with AutoPredictor, which involves applying the optimal combination of algorithms to each time series in your datasets. You can use CreateAutoPredictor to create new predictors or upgrade/retrain existing predictors. Creating new predictors The following parameters are required when creating a new predictor: PredictorName - A unique name for the predictor. DatasetGroupArn - The ARN of the dataset group used to train the predictor. ForecastFrequency - The granularity of your forecasts (hourly, daily, weekly, etc). ForecastHorizon - The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. When creating a new predictor, do not specify a value for ReferencePredictorArn. Upgrading and retraining predictors The following parameters are required when retraining or upgrading a predictor: PredictorName - A unique name for the predictor. ReferencePredictorArn - The ARN of the predictor to retrain or upgrade. When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and PredictorName.

INSERT INTO aws.forecast.auto_predictors (
PredictorName,
ForecastHorizon,
ForecastTypes,
ForecastDimensions,
ForecastFrequency,
DataConfig,
EncryptionConfig,
ReferencePredictorArn,
OptimizationMetric,
ExplainPredictor,
Tags,
MonitorConfig,
TimeAlignmentBoundary,
region
)
SELECT 
'{{ PredictorName }}' /* required */,
{{ ForecastHorizon }},
'{{ ForecastTypes }}',
'{{ ForecastDimensions }}',
'{{ ForecastFrequency }}',
'{{ DataConfig }}',
'{{ EncryptionConfig }}',
'{{ ReferencePredictorArn }}',
'{{ OptimizationMetric }}',
{{ ExplainPredictor }},
'{{ Tags }}',
'{{ MonitorConfig }}',
'{{ TimeAlignmentBoundary }}',
'{{ region }}'
RETURNING
predictor_arn
;

Manifest

# Description fields are for documentation purposes
- name: auto_predictors
props:
  - name: region
    value: "{{ region }}"
    description: Required parameter for the auto_predictors resource.
  - name: PredictorName
    value: "{{ PredictorName }}"
    description: |
      A unique name for the predictor
  - name: ForecastHorizon
    value: {{ ForecastHorizon }}
    description: |
      The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the TARGET_TIME_SERIES dataset length. If you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length. If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the forecast horizon parameter. You can meet this requirement by providing longer time-series in the dataset.
  - name: ForecastTypes
    value:
      - "{{ ForecastTypes }}"
    description: |
      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.
  - name: ForecastDimensions
    value:
      - "{{ ForecastDimensions }}"
    description: |
      An array of dimension (field) names that specify how to group the generated forecast. For example, if you are generating forecasts for item sales across all your stores, and your dataset contains a store_id field, you would specify store_id as a dimension to group sales forecasts for each store.
  - name: ForecastFrequency
    value: "{{ ForecastFrequency }}"
    description: |
      The frequency of predictions in a forecast. Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following: Minute - 1-59 Hour - 1-23 Day - 1-6 Week - 1-4 Month - 1-11 Year - 1 Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M". 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.
  - name: DataConfig
    description: |
      The data configuration for your dataset group and any additional datasets.
    value:
      DatasetGroupArn: "{{ DatasetGroupArn }}"
      AttributeConfigs:
        - AttributeName: "{{ AttributeName }}"
          Transformations: "{{ Transformations }}"
      AdditionalDatasets:
        - Name: "{{ Name }}"
          Configuration: "{{ Configuration }}"
  - name: EncryptionConfig
    description: |
      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.
    value:
      RoleArn: "{{ RoleArn }}"
      KMSKeyArn: "{{ KMSKeyArn }}"
  - name: ReferencePredictorArn
    value: "{{ ReferencePredictorArn }}"
    description: |
      The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a predictor. When creating a new predictor, do not specify a value for this parameter. When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and PredictorName. The value for PredictorName must be a unique predictor name.
  - name: OptimizationMetric
    value: "{{ OptimizationMetric }}"
    description: |
      The accuracy metric used to optimize the predictor.
    valid_values: ['WAPE', 'RMSE', 'AverageWeightedQuantileLoss', 'MASE', 'MAPE']
  - name: ExplainPredictor
    value: {{ ExplainPredictor }}
    description: |
      Create an Explainability resource for the predictor.
  - name: Tags
    description: |
      Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive. The following restrictions apply to tags: For each resource, each tag key must be unique and each tag key must have one value. Maximum number of tags per resource: 50. Maximum key length: 128 Unicode characters in UTF-8. Maximum value length: 256 Unicode characters in UTF-8. Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply. Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws as its prefix but the key does not, 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. You cannot edit or delete tag keys with this prefix.
    value:
      - Key: "{{ Key }}"
        Value: "{{ Value }}"
  - name: MonitorConfig
    description: |
      The configuration details for predictor monitoring. Provide a name for the monitor resource to enable predictor monitoring. Predictor monitoring allows you to see how your predictor's performance changes over time. For more information, see Predictor Monitoring.
    value:
      MonitorName: "{{ MonitorName }}"
  - name: TimeAlignmentBoundary
    description: |
      The time boundary Forecast uses to align and aggregate any data that doesn't align with your forecast frequency. Provide the unit of time and the time boundary as a key value pair. For more information on specifying a time boundary, see Specifying a Time Boundary. If you don't provide a time boundary, Forecast uses a set of Default Time Boundaries.
    value:
      Month: "{{ Month }}"
      DayOfMonth: {{ DayOfMonth }}
      DayOfWeek: "{{ DayOfWeek }}"
      Hour: {{ Hour }}