| Column Name | Column Data Type |
|---|
predictor_arn Required Input Column
The ARN of the predictor. | VARCHAR |
_aws_profile Input Column
The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role. | STRUCT(
"type" VARCHAR,
"name" VARCHAR,
"account_id" VARCHAR,
"via_profile_name" VARCHAR,
"assumed_role_arn" VARCHAR,
"organization" STRUCT(
"account_name" VARCHAR,
"id" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[],
"master_account" STRUCT(
"id" VARCHAR,
"email" VARCHAR
),
"parents" STRUCT(
"type" VARCHAR,
"id" VARCHAR,
"name" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[]
)[]
)
) |
Show child fields- _aws_profile.account_id
The AWS account id - _aws_profile.assumed_role_arn
The ARN of the assumed role - _aws_profile.name
The unique name of the profile. - _aws_profile.organization
Information about this profile's membership in the AWS organization. Show child fields- _aws_profile.organization.account_name
The name of account speciifed by the organization - _aws_profile.organization.id
The organization id - _aws_profile.organization.master_account
Show child fields- _aws_profile.organization.master_account.email
The organization master account email address - _aws_profile.organization.master_account.id
The organization master account id
- _aws_profile.organization.parents[]
Show child fields- _aws_profile.organization.parents[].id
The id of the parent - _aws_profile.organization.parents[].name
The name of the parent - _aws_profile.organization.parents[].tags[]
Show child fields- _aws_profile.organization.parents[].tags[].key
- _aws_profile.organization.parents[].tags[].value
- _aws_profile.organization.parents[].type
The type of parent can be an organization unit or a root
- _aws_profile.organization.tags[]
Show child fields- _aws_profile.organization.tags[].key
- _aws_profile.organization.tags[].value
- _aws_profile.type
The type of profile, either 'credentials' or 'assumed_role' - _aws_profile.via_profile_name
This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.
|
_aws_region Input Column
The AWS region to use. | VARCHAR |
algorithm_arn
The Amazon Resource Name (ARN) of the algorithm used for model training. | VARCHAR |
auto_ml_algorithm_arns
When PerformAutoML is specified, the ARN of the chosen algorithm. | VARCHAR[] |
Show child fields- auto_ml_algorithm_arns[]
|
auto_ml_override_strategy
The LatencyOptimized AutoML override strategy is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges. The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy. This parameter is only valid for predictors trained using AutoML. | VARCHAR |
creation_time
When the model training task was created. | TIMESTAMP_S |
dataset_import_job_arns
An array of the ARNs of the dataset import jobs used to import training data for the predictor. | VARCHAR[] |
Show child fields- dataset_import_job_arns[]
|
encryption_config
An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key. | STRUCT(
"role_arn" VARCHAR,
"kms_key_arn" VARCHAR
) |
Show child fields- encryption_config.kms_key_arn
The Amazon Resource Name (ARN) of the KMS key. - encryption_config.role_arn
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.
|
estimated_time_remaining_in_minutes
The estimated time remaining in minutes for the predictor training job to complete. | BIGINT |
evaluation_parameters
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. | STRUCT(
"number_of_backtest_windows" BIGINT,
"back_test_window_offset" BIGINT
) |
Show child fields- evaluation_parameters.back_test_window_offset
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 - evaluation_parameters.number_of_backtest_windows
The number of times to split the input data. The default is 1. Valid values are 1 through 5.
|
featurization_config
The featurization configuration. | STRUCT(
"forecast_frequency" VARCHAR,
"forecast_dimensions" VARCHAR[],
"featurizations" STRUCT(
"attribute_name" VARCHAR,
"featurization_pipeline" STRUCT(
"featurization_method_name" VARCHAR,
"featurization_method_parameters" MAP(VARCHAR, VARCHAR)
)[]
)[]
) |
Show child fields- featurization_config.featurizations[]
Show child fields- featurization_config.featurizations[].attribute_name
The name of the schema attribute that specifies the data field to be featurized. 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, and for the CUSTOM domain, the target is target_value. For more information, see howitworks-missing-values. - featurization_config.featurizations[].featurization_pipeline[]
Show child fields- featurization_config.featurizations[].featurization_pipeline[].featurization_method_name
The name of the method. The "filling" method is the only supported method. - featurization_config.featurizations[].featurization_pipeline[].featurization_method_parameters
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. Bold signifies the default value. -
aggregation: sum, avg, first, min, max -
frontfill: none -
middlefill: zero, nan (not a number), value, median, mean, min, max -
backfill: zero, nan, value, median, mean, min, max The 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, max -
backfill: zero, value, median, mean, min, max -
futurefill: zero, value, median, mean, min, max To 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".
- featurization_config.forecast_dimensions[]
- featurization_config.forecast_frequency
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 TARGET_TIME_SERIES dataset frequency.
|
forecast_horizon
The number of time-steps of the forecast. The forecast horizon is also called the prediction length. | BIGINT |
forecast_types
The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"] | VARCHAR[] |
Show child fields- forecast_types[]
|
hpo_config
The hyperparameter override values for the algorithm. | STRUCT(
"parameter_ranges" STRUCT(
"categorical_parameter_ranges" STRUCT(
"name" VARCHAR,
"values" VARCHAR[]
)[],
"continuous_parameter_ranges" STRUCT(
"name" VARCHAR,
"max_value" DOUBLE,
"min_value" DOUBLE,
"scaling_type" VARCHAR
)[],
"integer_parameter_ranges" STRUCT(
"name" VARCHAR,
"max_value" BIGINT,
"min_value" BIGINT,
"scaling_type" VARCHAR
)[]
)
) |
Show child fields- hpo_config.parameter_ranges
Specifies the ranges of valid values for the hyperparameters. Show child fields- hpo_config.parameter_ranges.categorical_parameter_ranges[]
Show child fields- hpo_config.parameter_ranges.categorical_parameter_ranges[].name
The name of the categorical hyperparameter to tune. - hpo_config.parameter_ranges.categorical_parameter_ranges[].values[]
- hpo_config.parameter_ranges.continuous_parameter_ranges[]
Show child fields- hpo_config.parameter_ranges.continuous_parameter_ranges[].max_value
The maximum tunable value of the hyperparameter. - hpo_config.parameter_ranges.continuous_parameter_ranges[].min_value
The minimum tunable value of the hyperparameter. - hpo_config.parameter_ranges.continuous_parameter_ranges[].name
The name of the hyperparameter to tune. - hpo_config.parameter_ranges.continuous_parameter_ranges[].scaling_type
The scale that hyperparameter tuning uses to search the hyperparameter range. Valid values: - Auto
-
Amazon Forecast hyperparameter tuning chooses the best scale for the hyperparameter. - Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. - Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have values greater than 0. - ReverseLogarithmic
-
hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling works only for ranges that are entirely within the range 0 <= x < 1.0. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- hpo_config.parameter_ranges.integer_parameter_ranges[]
Show child fields- hpo_config.parameter_ranges.integer_parameter_ranges[].max_value
The maximum tunable value of the hyperparameter. - hpo_config.parameter_ranges.integer_parameter_ranges[].min_value
The minimum tunable value of the hyperparameter. - hpo_config.parameter_ranges.integer_parameter_ranges[].name
The name of the hyperparameter to tune. - hpo_config.parameter_ranges.integer_parameter_ranges[].scaling_type
The scale that hyperparameter tuning uses to search the hyperparameter range. Valid values: - Auto
-
Amazon Forecast hyperparameter tuning chooses the best scale for the hyperparameter. - Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. - Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have values greater than 0. - ReverseLogarithmic
-
Not supported for IntegerParameterRange. Reverse logarithmic scaling works only for ranges that are entirely within the range 0 <= x < 1.0. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
|
input_data_config
Describes the dataset group that contains the data to use to train the predictor. | STRUCT(
"dataset_group_arn" VARCHAR,
"supplementary_features" STRUCT(
"name" VARCHAR,
"value" VARCHAR
)[]
) |
Show child fields- input_data_config.dataset_group_arn
The Amazon Resource Name (ARN) of the dataset group. - input_data_config.supplementary_features[]
Show child fields- input_data_config.supplementary_features[].name
The name of the feature. Valid values: "holiday" and "weather". - input_data_config.supplementary_features[].value
Weather Index To enable the Weather Index, set the value to "true" Holidays To enable Holidays, specify a country with one of the following two-letter country codes:
|
is_auto_predictor
Whether the predictor was created with CreateAutoPredictor. | BOOLEAN |
last_modification_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. | TIMESTAMP_S |
message
If an error occurred, an informational message about the error. | VARCHAR |
optimization_metric
The accuracy metric used to optimize the predictor. | VARCHAR |
perform_auto_ml
Whether the predictor is set to perform AutoML. | BOOLEAN |
perform_hpo
Whether the predictor is set to perform hyperparameter optimization (HPO). | BOOLEAN |
predictor_execution_details
Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You specify the number of backtests to perform when you call the operation. | STRUCT(
"predictor_executions" STRUCT(
"algorithm_arn" VARCHAR,
"test_windows" STRUCT(
"test_window_start" TIMESTAMP_S,
"test_window_end" TIMESTAMP_S,
"status" VARCHAR,
"message" VARCHAR
)[]
)[]
) |
Show child fields- predictor_execution_details.predictor_executions[]
Show child fields- predictor_execution_details.predictor_executions[].algorithm_arn
The ARN of the algorithm used to test the predictor. - predictor_execution_details.predictor_executions[].test_windows[]
Show child fields- predictor_execution_details.predictor_executions[].test_windows[].message
If the test failed, the reason why it failed. - predictor_execution_details.predictor_executions[].test_windows[].status
The status of the test. Possible status values are: -
ACTIVE -
CREATE_IN_PROGRESS -
CREATE_FAILED - predictor_execution_details.predictor_executions[].test_windows[].test_window_end
The time at which the test ended. - predictor_execution_details.predictor_executions[].test_windows[].test_window_start
The time at which the test began.
|
predictor_name
The name of the predictor. | VARCHAR |
status
The status of the predictor. States include: -
ACTIVE -
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED -
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED -
CREATE_STOPPING, CREATE_STOPPED The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast. | VARCHAR |
training_parameters
The default training parameters or overrides selected during model training. When running AutoML or choosing HPO with CNN-QR or DeepAR+, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes. | MAP(VARCHAR, VARCHAR) |