Table of Contents
A low-level client representing Amazon Forecast Service
Provides APIs for creating and managing Amazon Forecast resources.
import boto3
client = boto3.client('forecast')
These are the available methods:
Check if an operation can be paginated.
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:
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:
When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and PredictorName .
See also: AWS API Documentation
Request Syntax
response = client.create_auto_predictor(
PredictorName='string',
ForecastHorizon=123,
ForecastTypes=[
'string',
],
ForecastDimensions=[
'string',
],
ForecastFrequency='string',
DataConfig={
'DatasetGroupArn': 'string',
'AttributeConfigs': [
{
'AttributeName': 'string',
'Transformations': {
'string': 'string'
}
},
],
'AdditionalDatasets': [
{
'Name': 'string',
'Configuration': {
'string': [
'string',
]
}
},
]
},
EncryptionConfig={
'RoleArn': 'string',
'KMSKeyArn': 'string'
},
ReferencePredictorArn='string',
OptimizationMetric='WAPE'|'RMSE'|'AverageWeightedQuantileLoss'|'MASE'|'MAPE',
ExplainPredictor=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
A unique name for the predictor
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 .
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.
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.
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.
The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):
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" .
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 .
Holidays
To enable Holidays, set CountryCode to one of the following two-letter country codes:
An AWS Key Management Service (KMS) key and an AWS 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 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.
The Amazon Resource Name (ARN) of the KMS key.
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.
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:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'PredictorArn': 'string'
}
Response Structure
(dict) --
PredictorArn (string) --
The Amazon Resource Name (ARN) of the predictor.
Exceptions
Creates an Amazon Forecast dataset. The information about the dataset that you provide helps Forecast understand how to consume the data for model training. This includes the following:
After creating a dataset, you import your training data into it and add the dataset to a dataset group. You use the dataset group to create a predictor. For more information, see howitworks-datasets-groups .
To get a list of all your datasets, use the ListDatasets operation.
For example Forecast datasets, see the Amazon Forecast Sample GitHub repository .
Note
The Status of a dataset must be ACTIVE before you can import training data. Use the DescribeDataset operation to get the status.
See also: AWS API Documentation
Request Syntax
response = client.create_dataset(
DatasetName='string',
Domain='RETAIL'|'CUSTOM'|'INVENTORY_PLANNING'|'EC2_CAPACITY'|'WORK_FORCE'|'WEB_TRAFFIC'|'METRICS',
DatasetType='TARGET_TIME_SERIES'|'RELATED_TIME_SERIES'|'ITEM_METADATA',
DataFrequency='string',
Schema={
'Attributes': [
{
'AttributeName': 'string',
'AttributeType': 'string'|'integer'|'float'|'timestamp'|'geolocation'
},
]
},
EncryptionConfig={
'RoleArn': 'string',
'KMSKeyArn': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
A name for the dataset.
[REQUIRED]
The domain associated with the dataset. When you add a dataset to a dataset group, this value and the value specified for the Domain parameter of the CreateDatasetGroup operation must match.
The Domain and DatasetType that you choose determine the fields that must be present in the training data that you import to the dataset. For example, if you choose the RETAIL domain and TARGET_TIME_SERIES as the DatasetType , Amazon Forecast requires item_id , timestamp , and demand fields to be present in your data. For more information, see howitworks-datasets-groups .
[REQUIRED]
The dataset type. Valid values depend on the chosen Domain .
The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets.
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, "D" indicates every day and "15min" indicates every 15 minutes.
[REQUIRED]
The schema for the dataset. The schema attributes and their order must match the fields in your data. The dataset Domain and DatasetType that you choose determine the minimum required fields in your training data. For information about the required fields for a specific dataset domain and type, see howitworks-domains-ds-types .
An array of attributes specifying the name and type of each field in a dataset.
An attribute of a schema, which defines a dataset field. A schema attribute is required for every field in a dataset. The Schema object contains an array of SchemaAttribute objects.
The name of the dataset field.
The data type of the field.
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.
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.
The Amazon Resource Name (ARN) of the KMS key.
The optional metadata that you apply to the dataset 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:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'DatasetArn': 'string'
}
Response Structure
(dict) --
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset.
Exceptions
Creates a dataset group, which holds a collection of related datasets. You can add datasets to the dataset group when you create the dataset group, or later by using the UpdateDatasetGroup operation.
After creating a dataset group and adding datasets, you use the dataset group when you create a predictor. For more information, see howitworks-datasets-groups .
To get a list of all your datasets groups, use the ListDatasetGroups operation.
Note
The Status of a dataset group must be ACTIVE before you can use the dataset group to create a predictor. To get the status, use the DescribeDatasetGroup operation.
See also: AWS API Documentation
Request Syntax
response = client.create_dataset_group(
DatasetGroupName='string',
Domain='RETAIL'|'CUSTOM'|'INVENTORY_PLANNING'|'EC2_CAPACITY'|'WORK_FORCE'|'WEB_TRAFFIC'|'METRICS',
DatasetArns=[
'string',
],
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
A name for the dataset group.
[REQUIRED]
The domain associated with the dataset group. When you add a dataset to a dataset group, this value and the value specified for the Domain parameter of the CreateDataset operation must match.
The Domain and DatasetType that you choose determine the fields that must be present in training data that you import to a dataset. For example, if you choose the RETAIL domain and TARGET_TIME_SERIES as the DatasetType , Amazon Forecast requires that item_id , timestamp , and demand fields are present in your data. For more information, see howitworks-datasets-groups .
An array of Amazon Resource Names (ARNs) of the datasets that you want to include in the dataset group.
The optional metadata that you apply to the dataset group 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:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'DatasetGroupArn': 'string'
}
Response Structure
(dict) --
DatasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group.
Exceptions
Imports your training data to an Amazon Forecast dataset. You provide the location of your training data in an Amazon Simple Storage Service (Amazon S3) bucket and the Amazon Resource Name (ARN) of the dataset that you want to import the data to.
You must specify a DataSource object that includes an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data, as Amazon Forecast makes a copy of your data and processes it in an internal AWS system. For more information, see aws-forecast-iam-roles .
The training data must be in CSV format. The delimiter must be a comma (,).
You can specify the path to a specific CSV file, the S3 bucket, or to a folder in the S3 bucket. For the latter two cases, Amazon Forecast imports all files up to the limit of 10,000 files.
Because dataset imports are not aggregated, your most recent dataset import is the one that is used when training a predictor or generating a forecast. Make sure that your most recent dataset import contains all of the data you want to model off of, and not just the new data collected since the previous import.
To get a list of all your dataset import jobs, filtered by specified criteria, use the ListDatasetImportJobs operation.
See also: AWS API Documentation
Request Syntax
response = client.create_dataset_import_job(
DatasetImportJobName='string',
DatasetArn='string',
DataSource={
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
TimestampFormat='string',
TimeZone='string',
UseGeolocationForTimeZone=True|False,
GeolocationFormat='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name for the dataset import job. We recommend including the current timestamp in the name, for example, 20190721DatasetImport . This can help you avoid getting a ResourceAlreadyExistsException exception.
[REQUIRED]
The Amazon Resource Name (ARN) of the Amazon Forecast dataset that you want to import data to.
[REQUIRED]
The location of the training data to import and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data. The training data must be stored in an Amazon S3 bucket.
If encryption is used, DataSource must include an AWS Key Management Service (KMS) key and the IAM role must allow Amazon Forecast permission to access the key. The KMS key and IAM role must match those specified in the EncryptionConfig parameter of the CreateDataset operation.
The path to the data stored in an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the data.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
The format of timestamps in the dataset. The format that you specify depends on the DataFrequency specified when the dataset was created. The following formats are supported
If the format isn't specified, Amazon Forecast expects the format to be "yyyy-MM-dd HH:mm:ss".
A single time zone for every item in your dataset. This option is ideal for datasets with all timestamps within a single time zone, or if all timestamps are normalized to a single time zone.
Refer to the Joda-Time API for a complete list of valid time zone names.
The format of the geolocation attribute. The geolocation attribute can be formatted in one of two ways:
The optional metadata that you apply to the dataset import job 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:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'DatasetImportJobArn': 'string'
}
Response Structure
(dict) --
DatasetImportJobArn (string) --
The Amazon Resource Name (ARN) of the dataset import job.
Exceptions
Note
Explainability is only available for Forecasts and Predictors generated from an AutoPredictor ( CreateAutoPredictor )
Creates an Amazon Forecast Explainability.
Explainability helps you better understand how the attributes in your datasets impact forecast. Amazon Forecast uses a metric called Impact scores to quantify the relative impact of each attribute and determine whether they increase or decrease forecast values.
To enable Forecast Explainability, your predictor must include at least one of the following: related time series, item metadata, or additional datasets like Holidays and the Weather Index.
CreateExplainability accepts either a Predictor ARN or Forecast ARN. To receive aggregated Impact scores for all time series and time points in your datasets, provide a Predictor ARN. To receive Impact scores for specific time series and time points, provide a Forecast ARN.
CreateExplainability with a Predictor ARN
Note
You can only have one Explainability resource per predictor. If you already enabled ExplainPredictor in CreateAutoPredictor , that predictor already has an Explainability resource.
The following parameters are required when providing a Predictor ARN:
Do not specify a value for the following parameters:
CreateExplainability with a Forecast ARN
Note
You can specify a maximum of 50 time series and 500 time points.
The following parameters are required when providing a Predictor ARN:
If you set TimeSeriesGranularity to “SPECIFIC”, you must also provide the following:
If you set TimePointGranularity to “SPECIFIC”, you must also provide the following:
See also: AWS API Documentation
Request Syntax
response = client.create_explainability(
ExplainabilityName='string',
ResourceArn='string',
ExplainabilityConfig={
'TimeSeriesGranularity': 'ALL'|'SPECIFIC',
'TimePointGranularity': 'ALL'|'SPECIFIC'
},
DataSource={
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
Schema={
'Attributes': [
{
'AttributeName': 'string',
'AttributeType': 'string'|'integer'|'float'|'timestamp'|'geolocation'
},
]
},
EnableVisualization=True|False,
StartDateTime='string',
EndDateTime='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
A unique name for the Explainability.
[REQUIRED]
The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability.
[REQUIRED]
The configuration settings that define the granularity of time series and time points for the Explainability.
To create an Explainability for all time series in your datasets, use ALL . To create an Explainability for specific time series in your datasets, use SPECIFIC .
Specify time series by uploading a CSV file to an Amazon S3 bucket and set the location within the DataDestination data type.
To create an Explainability for all time points in your forecast horizon, use ALL . To create an Explainability for specific time points in your forecast horizon, use SPECIFIC .
Specify time points with the StartDateTime and EndDateTime parameters within the CreateExplainability operation.
The source of your data, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the data and, optionally, an AWS Key Management Service (KMS) key.
The path to the data stored in an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the data.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Defines the fields of a dataset.
An array of attributes specifying the name and type of each field in a dataset.
An attribute of a schema, which defines a dataset field. A schema attribute is required for every field in a dataset. The Schema object contains an array of SchemaAttribute objects.
The name of the dataset field.
The data type of the field.
If TimePointGranularity is set to SPECIFIC , define the first point for the Explainability.
Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)
If TimePointGranularity is set to SPECIFIC , define the last time point for the Explainability.
Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)
Optional metadata to help you categorize and organize your resources. 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:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'ExplainabilityArn': 'string'
}
Response Structure
(dict) --
ExplainabilityArn (string) --
The Amazon Resource Name (ARN) of the Explainability.
Exceptions
Exports an Explainability resource created by the CreateExplainability operation. Exported files are exported to an Amazon Simple Storage Service (Amazon S3) bucket.
You must specify a DataDestination object that includes an Amazon S3 bucket and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles .
Note
The Status of the export job must be ACTIVE before you can access the export in your Amazon S3 bucket. To get the status, use the DescribeExplainabilityExport operation.
See also: AWS API Documentation
Request Syntax
response = client.create_explainability_export(
ExplainabilityExportName='string',
ExplainabilityArn='string',
Destination={
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
A unique name for the Explainability export.
[REQUIRED]
The Amazon Resource Name (ARN) of the Explainability to export.
[REQUIRED]
The destination for an export job. Provide an S3 path, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an AWS Key Management Service (KMS) key (optional).
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Optional metadata to help you categorize and organize your resources. 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:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'ExplainabilityExportArn': 'string'
}
Response Structure
(dict) --
ExplainabilityExportArn (string) --
The Amazon Resource Name (ARN) of the export.
Exceptions
Creates a forecast for each item in the TARGET_TIME_SERIES dataset that was used to train the predictor. This is known as inference. To retrieve the forecast for a single item at low latency, use the operation. To export the complete forecast into your Amazon Simple Storage Service (Amazon S3) bucket, use the CreateForecastExportJob operation.
The range of the forecast is determined by the ForecastHorizon value, which you specify in the CreatePredictor request. When you query a forecast, you can request a specific date range within the forecast.
To get a list of all your forecasts, use the ListForecasts operation.
Note
The forecasts generated by Amazon Forecast are in the same time zone as the dataset that was used to create the predictor.
For more information, see howitworks-forecast .
Note
The Status of the forecast must be ACTIVE before you can query or export the forecast. Use the DescribeForecast operation to get the status.
See also: AWS API Documentation
Request Syntax
response = client.create_forecast(
ForecastName='string',
PredictorArn='string',
ForecastTypes=[
'string',
],
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
A name for the forecast.
[REQUIRED]
The Amazon Resource Name (ARN) of the predictor to use to generate the forecast.
The quantiles at which probabilistic forecasts are generated. You can currently specify up to 5 quantiles per forecast . Accepted values include 0.01 to 0.99 (increments of .01 only) and mean . The mean forecast is different from the median (0.50) when the distribution is not symmetric (for example, Beta and Negative Binomial). The default value is ["0.1", "0.5", "0.9"] .
The optional metadata that you apply to the forecast 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:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'ForecastArn': 'string'
}
Response Structure
(dict) --
ForecastArn (string) --
The Amazon Resource Name (ARN) of the forecast.
Exceptions
Exports a forecast created by the CreateForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket. The forecast file name will match the following conventions:
<ForecastExportJobName>_<ExportTimestamp>_<PartNumber>
where the <ExportTimestamp> component is in Java SimpleDateFormat (yyyy-MM-ddTHH-mm-ssZ).
You must specify a DataDestination object that includes an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles .
For more information, see howitworks-forecast .
To get a list of all your forecast export jobs, use the ListForecastExportJobs operation.
Note
The Status of the forecast export job must be ACTIVE before you can access the forecast in your Amazon S3 bucket. To get the status, use the DescribeForecastExportJob operation.
See also: AWS API Documentation
Request Syntax
response = client.create_forecast_export_job(
ForecastExportJobName='string',
ForecastArn='string',
Destination={
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name for the forecast export job.
[REQUIRED]
The Amazon Resource Name (ARN) of the forecast that you want to export.
[REQUIRED]
The location where you want to save the forecast and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the location. The forecast must be exported to an Amazon S3 bucket.
If encryption is used, Destination must include an AWS Key Management Service (KMS) key. The IAM role must allow Amazon Forecast permission to access the key.
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
The optional metadata that you apply to the forecast export job 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:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'ForecastExportJobArn': 'string'
}
Response Structure
(dict) --
ForecastExportJobArn (string) --
The Amazon Resource Name (ARN) of the export job.
Exceptions
Note
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:
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
Request Syntax
response = client.create_predictor(
PredictorName='string',
AlgorithmArn='string',
ForecastHorizon=123,
ForecastTypes=[
'string',
],
PerformAutoML=True|False,
AutoMLOverrideStrategy='LatencyOptimized'|'AccuracyOptimized',
PerformHPO=True|False,
TrainingParameters={
'string': 'string'
},
EvaluationParameters={
'NumberOfBacktestWindows': 123,
'BackTestWindowOffset': 123
},
HPOConfig={
'ParameterRanges': {
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MaxValue': 123.0,
'MinValue': 123.0,
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'IntegerParameterRanges': [
{
'Name': 'string',
'MaxValue': 123,
'MinValue': 123,
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
]
}
},
InputDataConfig={
'DatasetGroupArn': 'string',
'SupplementaryFeatures': [
{
'Name': 'string',
'Value': 'string'
},
]
},
FeaturizationConfig={
'ForecastFrequency': 'string',
'ForecastDimensions': [
'string',
],
'Featurizations': [
{
'AttributeName': 'string',
'FeaturizationPipeline': [
{
'FeaturizationMethodName': 'filling',
'FeaturizationMethodParameters': {
'string': 'string'
}
},
]
},
]
},
EncryptionConfig={
'RoleArn': 'string',
'KMSKeyArn': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
OptimizationMetric='WAPE'|'RMSE'|'AverageWeightedQuantileLoss'|'MASE'|'MAPE'
)
[REQUIRED]
A name for the predictor.
The Amazon Resource Name (ARN) of the algorithm to use for model training. Required if PerformAutoML is not set to true .
Supported algorithms:
[REQUIRED]
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.
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"] .
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.
Note
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.
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:
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 .
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.
The number of times to split the input data. The default is 1. Valid values are 1 through 5.
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
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.
Specifies the ranges of valid values for the hyperparameters.
Specifies the tunable range for each categorical hyperparameter.
Specifies a categorical hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
The name of the categorical hyperparameter to tune.
A list of the tunable categories for the hyperparameter.
Specifies the tunable range for each continuous hyperparameter.
Specifies a continuous hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
The name of the hyperparameter to tune.
The maximum tunable value of the hyperparameter.
The minimum tunable value of the hyperparameter.
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:
Specifies the tunable range for each integer hyperparameter.
Specifies an integer hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
The name of the hyperparameter to tune.
The maximum tunable value of the hyperparameter.
The minimum tunable value of the hyperparameter.
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:
[REQUIRED]
Describes the dataset group that contains the data to use to train the predictor.
The Amazon Resource Name (ARN) of the dataset group.
An array of supplementary features. The only supported feature is a holiday calendar.
Note
This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor , see AdditionalDataset .
Describes a supplementary feature of a dataset group. This object is part of the InputDataConfig object. Forecast supports the Weather Index and Holidays built-in featurizations.
Weather Index
The Amazon Forecast Weather Index is a built-in featurization 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 featurization that incorporates a feature-engineered dataset of 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 feature. Valid values: "holiday" and "weather" .
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:
[REQUIRED]
The featurization configuration.
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.
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.
An array of featurization (transformation) information for the fields of a dataset.
Note
This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor , see AttributeConfig .
Provides featurization (transformation) information for a dataset field. This object is part of the FeaturizationConfig object.
For example:
{"AttributeName": "demand",
FeaturizationPipeline [ {
"FeaturizationMethodName": "filling",
"FeaturizationMethodParameters": {"aggregation": "avg", "backfill": "nan"}
} ]
}
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 .
An array of one FeaturizationMethod object that specifies the feature transformation method.
Provides information about the method that featurizes (transforms) a dataset field. The method is part of the FeaturizationPipeline of the Featurization object.
The following is an example of how you specify a FeaturizationMethod object.
{"FeaturizationMethodName": "filling",
"FeaturizationMethodParameters": {"aggregation": "sum", "middlefill": "zero", "backfill": "zero"}
}
The name of the method. The "filling" method is the only supported method.
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.
The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):
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" .
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.
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.
The Amazon Resource Name (ARN) of the KMS key.
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:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'PredictorArn': 'string'
}
Response Structure
(dict) --
PredictorArn (string) --
The Amazon Resource Name (ARN) of the predictor.
Exceptions
Exports backtest forecasts and accuracy metrics generated by the CreateAutoPredictor or CreatePredictor operations. Two folders containing CSV files are exported to your specified S3 bucket.
The export file names will match the following conventions:
<ExportJobName>_<ExportTimestamp>_<PartNumber>.csv
The <ExportTimestamp> component is in Java SimpleDate format (yyyy-MM-ddTHH-mm-ssZ).
You must specify a DataDestination object that includes an Amazon S3 bucket and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles .
Note
The Status of the export job must be ACTIVE before you can access the export in your Amazon S3 bucket. To get the status, use the DescribePredictorBacktestExportJob operation.
See also: AWS API Documentation
Request Syntax
response = client.create_predictor_backtest_export_job(
PredictorBacktestExportJobName='string',
PredictorArn='string',
Destination={
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name for the backtest export job.
[REQUIRED]
The Amazon Resource Name (ARN) of the predictor that you want to export.
[REQUIRED]
The destination for an export job. Provide an S3 path, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an AWS Key Management Service (KMS) key (optional).
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Optional metadata to help you categorize and organize your backtests. 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:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'PredictorBacktestExportJobArn': 'string'
}
Response Structure
(dict) --
PredictorBacktestExportJobArn (string) --
The Amazon Resource Name (ARN) of the predictor backtest export job that you want to export.
Exceptions
Deletes an Amazon Forecast dataset that was created using the CreateDataset operation. You can only delete datasets that have a status of ACTIVE or CREATE_FAILED . To get the status use the DescribeDataset operation.
Note
Forecast does not automatically update any dataset groups that contain the deleted dataset. In order to update the dataset group, use the operation, omitting the deleted dataset's ARN.
See also: AWS API Documentation
Request Syntax
response = client.delete_dataset(
DatasetArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset to delete.
Exceptions
Deletes a dataset group created using the CreateDatasetGroup operation. You can only delete dataset groups that have a status of ACTIVE , CREATE_FAILED , or UPDATE_FAILED . To get the status, use the DescribeDatasetGroup operation.
This operation deletes only the dataset group, not the datasets in the group.
See also: AWS API Documentation
Request Syntax
response = client.delete_dataset_group(
DatasetGroupArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset group to delete.
Exceptions
Deletes a dataset import job created using the CreateDatasetImportJob operation. You can delete only dataset import jobs that have a status of ACTIVE or CREATE_FAILED . To get the status, use the DescribeDatasetImportJob operation.
See also: AWS API Documentation
Request Syntax
response = client.delete_dataset_import_job(
DatasetImportJobArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset import job to delete.
Exceptions
Deletes an Explainability resource.
You can delete only predictor that have a status of ACTIVE or CREATE_FAILED . To get the status, use the DescribeExplainability operation.
See also: AWS API Documentation
Request Syntax
response = client.delete_explainability(
ExplainabilityArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the Explainability resource to delete.
Exceptions
Deletes an Explainability export.
See also: AWS API Documentation
Request Syntax
response = client.delete_explainability_export(
ExplainabilityExportArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the Explainability export to delete.
Exceptions
Deletes a forecast created using the CreateForecast operation. You can delete only forecasts that have a status of ACTIVE or CREATE_FAILED . To get the status, use the DescribeForecast operation.
You can't delete a forecast while it is being exported. After a forecast is deleted, you can no longer query the forecast.
See also: AWS API Documentation
Request Syntax
response = client.delete_forecast(
ForecastArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the forecast to delete.
Exceptions
Deletes a forecast export job created using the CreateForecastExportJob operation. You can delete only export jobs that have a status of ACTIVE or CREATE_FAILED . To get the status, use the DescribeForecastExportJob operation.
See also: AWS API Documentation
Request Syntax
response = client.delete_forecast_export_job(
ForecastExportJobArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the forecast export job to delete.
Exceptions
Deletes a predictor created using the DescribePredictor or CreatePredictor operations. You can delete only predictor that have a status of ACTIVE or CREATE_FAILED . To get the status, use the DescribePredictor operation.
See also: AWS API Documentation
Request Syntax
response = client.delete_predictor(
PredictorArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the predictor to delete.
Exceptions
Deletes a predictor backtest export job.
See also: AWS API Documentation
Request Syntax
response = client.delete_predictor_backtest_export_job(
PredictorBacktestExportJobArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the predictor backtest export job to delete.
Exceptions
Deletes an entire resource tree. This operation will delete the parent resource and its child resources.
Child resources are resources that were created from another resource. For example, when a forecast is generated from a predictor, the forecast is the child resource and the predictor is the parent resource.
Amazon Forecast resources possess the following parent-child resource hierarchies:
Note
DeleteResourceTree will only delete Amazon Forecast resources, and will not delete datasets or exported files stored in Amazon S3.
See also: AWS API Documentation
Request Syntax
response = client.delete_resource_tree(
ResourceArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the parent resource to delete. All child resources of the parent resource will also be deleted.
Exceptions
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'
}
}
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.
The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):
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" .
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 .
Holidays
To enable Holidays, set CountryCode to one of the following two-letter country codes:
An AWS Key Management Service (KMS) key and an AWS 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 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.
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:
In 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:
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:
Exceptions
Describes an Amazon Forecast dataset created using the CreateDataset operation.
In addition to listing the parameters specified in the CreateDataset request, this operation includes the following dataset properties:
See also: AWS API Documentation
Request Syntax
response = client.describe_dataset(
DatasetArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset.
{
'DatasetArn': 'string',
'DatasetName': 'string',
'Domain': 'RETAIL'|'CUSTOM'|'INVENTORY_PLANNING'|'EC2_CAPACITY'|'WORK_FORCE'|'WEB_TRAFFIC'|'METRICS',
'DatasetType': 'TARGET_TIME_SERIES'|'RELATED_TIME_SERIES'|'ITEM_METADATA',
'DataFrequency': 'string',
'Schema': {
'Attributes': [
{
'AttributeName': 'string',
'AttributeType': 'string'|'integer'|'float'|'timestamp'|'geolocation'
},
]
},
'EncryptionConfig': {
'RoleArn': 'string',
'KMSKeyArn': 'string'
},
'Status': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
}
Response Structure
The Amazon Resource Name (ARN) of the dataset.
The name of the dataset.
The domain associated with the dataset.
The dataset type.
The frequency of data collection.
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, "M" indicates every month and "30min" indicates every 30 minutes.
An array of SchemaAttribute objects that specify the dataset fields. Each SchemaAttribute specifies the name and data type of a field.
An array of attributes specifying the name and type of each field in a dataset.
An attribute of a schema, which defines a dataset field. A schema attribute is required for every field in a dataset. The Schema object contains an array of SchemaAttribute objects.
The name of the dataset field.
The data type of the field.
The AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
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.
The Amazon Resource Name (ARN) of the KMS key.
The status of the dataset. States include:
The UPDATE states apply while data is imported to the dataset from a call to the CreateDatasetImportJob operation and reflect the status of the dataset import job. For example, when the import job status is CREATE_IN_PROGRESS , the status of the dataset is UPDATE_IN_PROGRESS .
Note
The Status of the dataset must be ACTIVE before you can import training data.
When the dataset was created.
When you create a dataset, LastModificationTime is the same as CreationTime . While data is being imported to the dataset, LastModificationTime is the current time of the DescribeDataset call. After a CreateDatasetImportJob operation has finished, LastModificationTime is when the import job completed or failed.
Exceptions
Describes a dataset group created using the CreateDatasetGroup operation.
In addition to listing the parameters provided in the CreateDatasetGroup request, this operation includes the following properties:
See also: AWS API Documentation
Request Syntax
response = client.describe_dataset_group(
DatasetGroupArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset group.
{
'DatasetGroupName': 'string',
'DatasetGroupArn': 'string',
'DatasetArns': [
'string',
],
'Domain': 'RETAIL'|'CUSTOM'|'INVENTORY_PLANNING'|'EC2_CAPACITY'|'WORK_FORCE'|'WEB_TRAFFIC'|'METRICS',
'Status': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
}
Response Structure
The name of the dataset group.
The ARN of the dataset group.
An array of Amazon Resource Names (ARNs) of the datasets contained in the dataset group.
The domain associated with the dataset group.
The status of the dataset group. States include:
The UPDATE states apply when you call the UpdateDatasetGroup operation.
Note
The Status of the dataset group must be ACTIVE before you can use the dataset group to create a predictor.
When the dataset group was created.
When the dataset group was created or last updated from a call to the UpdateDatasetGroup operation. While the dataset group is being updated, LastModificationTime is the current time of the DescribeDatasetGroup call.
Exceptions
Describes a dataset import job created using the CreateDatasetImportJob operation.
In addition to listing the parameters provided in the CreateDatasetImportJob request, this operation includes the following properties:
See also: AWS API Documentation
Request Syntax
response = client.describe_dataset_import_job(
DatasetImportJobArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset import job.
{
'DatasetImportJobName': 'string',
'DatasetImportJobArn': 'string',
'DatasetArn': 'string',
'TimestampFormat': 'string',
'TimeZone': 'string',
'UseGeolocationForTimeZone': True|False,
'GeolocationFormat': 'string',
'DataSource': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'EstimatedTimeRemainingInMinutes': 123,
'FieldStatistics': {
'string': {
'Count': 123,
'CountDistinct': 123,
'CountNull': 123,
'CountNan': 123,
'Min': 'string',
'Max': 'string',
'Avg': 123.0,
'Stddev': 123.0,
'CountLong': 123,
'CountDistinctLong': 123,
'CountNullLong': 123,
'CountNanLong': 123
}
},
'DataSize': 123.0,
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
}
Response Structure
The name of the dataset import job.
The ARN of the dataset import job.
The Amazon Resource Name (ARN) of the dataset that the training data was imported to.
The format of timestamps in the dataset. The format that you specify depends on the DataFrequency specified when the dataset was created. The following formats are supported
The single time zone applied to every item in the dataset
Whether TimeZone is automatically derived from the geolocation attribute.
The format of the geolocation attribute. Valid Values:"LAT_LONG" and "CC_POSTALCODE" .
The location of the training data to import and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data.
If encryption is used, DataSource includes an AWS Key Management Service (KMS) key.
The path to the data stored in an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the data.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
The estimated time remaining in minutes for the dataset import job to complete.
Statistical information about each field in the input data.
Provides statistics for each data field imported into to an Amazon Forecast dataset with the CreateDatasetImportJob operation.
The number of values in the field. If the response value is -1, refer to CountLong .
The number of distinct values in the field. If the response value is -1, refer to CountDistinctLong .
The number of null values in the field. If the response value is -1, refer to CountNullLong .
The number of NAN (not a number) values in the field. If the response value is -1, refer to CountNanLong .
For a numeric field, the minimum value in the field.
For a numeric field, the maximum value in the field.
For a numeric field, the average value in the field.
For a numeric field, the standard deviation.
The number of values in the field. CountLong is used instead of Count if the value is greater than 2,147,483,647.
The number of distinct values in the field. CountDistinctLong is used instead of CountDistinct if the value is greater than 2,147,483,647.
The number of null values in the field. CountNullLong is used instead of CountNull if the value is greater than 2,147,483,647.
The number of NAN (not a number) values in the field. CountNanLong is used instead of CountNan if the value is greater than 2,147,483,647.
The size of the dataset in gigabytes (GB) after the import job has finished.
The status of the dataset import job. States include:
If an error occurred, an informational message about the error.
When the dataset import job was created.
The last time the resource was modified. The timestamp depends on the status of the job:
Exceptions
Describes an Explainability resource created using the CreateExplainability operation.
See also: AWS API Documentation
Request Syntax
response = client.describe_explainability(
ExplainabilityArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the Explaianability to describe.
{
'ExplainabilityArn': 'string',
'ExplainabilityName': 'string',
'ResourceArn': 'string',
'ExplainabilityConfig': {
'TimeSeriesGranularity': 'ALL'|'SPECIFIC',
'TimePointGranularity': 'ALL'|'SPECIFIC'
},
'EnableVisualization': True|False,
'DataSource': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Schema': {
'Attributes': [
{
'AttributeName': 'string',
'AttributeType': 'string'|'integer'|'float'|'timestamp'|'geolocation'
},
]
},
'StartDateTime': 'string',
'EndDateTime': 'string',
'EstimatedTimeRemainingInMinutes': 123,
'Message': 'string',
'Status': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
}
Response Structure
The Amazon Resource Name (ARN) of the Explainability.
The name of the Explainability.
The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability resource.
The configuration settings that define the granularity of time series and time points for the Explainability.
To create an Explainability for all time series in your datasets, use ALL . To create an Explainability for specific time series in your datasets, use SPECIFIC .
Specify time series by uploading a CSV file to an Amazon S3 bucket and set the location within the DataDestination data type.
To create an Explainability for all time points in your forecast horizon, use ALL . To create an Explainability for specific time points in your forecast horizon, use SPECIFIC .
Specify time points with the StartDateTime and EndDateTime parameters within the CreateExplainability operation.
Whether the visualization was enabled for the Explainability resource.
The source of your data, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the data and, optionally, an AWS Key Management Service (KMS) key.
The path to the data stored in an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the data.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Defines the fields of a dataset.
An array of attributes specifying the name and type of each field in a dataset.
An attribute of a schema, which defines a dataset field. A schema attribute is required for every field in a dataset. The Schema object contains an array of SchemaAttribute objects.
The name of the dataset field.
The data type of the field.
If TimePointGranularity is set to SPECIFIC , the first time point in the Explainability.
If TimePointGranularity is set to SPECIFIC , the last time point in the Explainability.
The estimated time remaining in minutes for the CreateExplainability job to complete.
If an error occurred, a message about the error.
The status of the Explainability resource. States include:
When the Explainability resource was created.
The last time the resource was modified. The timestamp depends on the status of the job:
Exceptions
Describes an Explainability export created using the CreateExplainabilityExport operation.
See also: AWS API Documentation
Request Syntax
response = client.describe_explainability_export(
ExplainabilityExportArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the Explainability export.
{
'ExplainabilityExportArn': 'string',
'ExplainabilityExportName': 'string',
'ExplainabilityArn': 'string',
'Destination': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Message': 'string',
'Status': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
}
Response Structure
The Amazon Resource Name (ARN) of the Explainability export.
The name of the Explainability export.
The Amazon Resource Name (ARN) of the Explainability.
The destination for an export job. Provide an S3 path, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an AWS Key Management Service (KMS) key (optional).
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Information about any errors that occurred during the export.
The status of the Explainability export. States include:
When the Explainability export was created.
The last time the resource was modified. The timestamp depends on the status of the job:
Exceptions
Describes a forecast created using the CreateForecast operation.
In addition to listing the properties provided in the CreateForecast request, this operation lists the following properties:
See also: AWS API Documentation
Request Syntax
response = client.describe_forecast(
ForecastArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the forecast.
{
'ForecastArn': 'string',
'ForecastName': 'string',
'ForecastTypes': [
'string',
],
'PredictorArn': 'string',
'DatasetGroupArn': 'string',
'EstimatedTimeRemainingInMinutes': 123,
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
}
Response Structure
The forecast ARN as specified in the request.
The name of the forecast.
The quantiles at which probabilistic forecasts were generated.
The ARN of the predictor used to generate the forecast.
The ARN of the dataset group that provided the data used to train the predictor.
The estimated time remaining in minutes for the forecast job to complete.
The status of the forecast. States include:
Note
The Status of the forecast must be ACTIVE before you can query or export the forecast.
If an error occurred, an informational message about the error.
When the forecast creation task was created.
The last time the resource was modified. The timestamp depends on the status of the job:
Exceptions
Describes a forecast export job created using the CreateForecastExportJob operation.
In addition to listing the properties provided by the user in the CreateForecastExportJob request, this operation lists the following properties:
See also: AWS API Documentation
Request Syntax
response = client.describe_forecast_export_job(
ForecastExportJobArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the forecast export job.
{
'ForecastExportJobArn': 'string',
'ForecastExportJobName': 'string',
'ForecastArn': 'string',
'Destination': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Message': 'string',
'Status': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
}
Response Structure
The ARN of the forecast export job.
The name of the forecast export job.
The Amazon Resource Name (ARN) of the exported forecast.
The path to the Amazon Simple Storage Service (Amazon S3) bucket where the forecast is exported.
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
If an error occurred, an informational message about the error.
The status of the forecast export job. States include:
Note
The Status of the forecast export job must be ACTIVE before you can access the forecast in your S3 bucket.
When the forecast export job was created.
The last time the resource was modified. The timestamp depends on the status of the job:
Exceptions
Note
This operation is only valid for legacy predictors created with CreatePredictor. If you are not using a legacy predictor, use DescribeAutoPredictor .
Describes a predictor created using the CreatePredictor operation.
In addition to listing the properties provided in the CreatePredictor request, this operation lists the following properties:
See also: AWS API Documentation
Request Syntax
response = client.describe_predictor(
PredictorArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the predictor that you want information about.
{
'PredictorArn': 'string',
'PredictorName': 'string',
'AlgorithmArn': 'string',
'AutoMLAlgorithmArns': [
'string',
],
'ForecastHorizon': 123,
'ForecastTypes': [
'string',
],
'PerformAutoML': True|False,
'AutoMLOverrideStrategy': 'LatencyOptimized'|'AccuracyOptimized',
'PerformHPO': True|False,
'TrainingParameters': {
'string': 'string'
},
'EvaluationParameters': {
'NumberOfBacktestWindows': 123,
'BackTestWindowOffset': 123
},
'HPOConfig': {
'ParameterRanges': {
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MaxValue': 123.0,
'MinValue': 123.0,
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'IntegerParameterRanges': [
{
'Name': 'string',
'MaxValue': 123,
'MinValue': 123,
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
]
}
},
'InputDataConfig': {
'DatasetGroupArn': 'string',
'SupplementaryFeatures': [
{
'Name': 'string',
'Value': 'string'
},
]
},
'FeaturizationConfig': {
'ForecastFrequency': 'string',
'ForecastDimensions': [
'string',
],
'Featurizations': [
{
'AttributeName': 'string',
'FeaturizationPipeline': [
{
'FeaturizationMethodName': 'filling',
'FeaturizationMethodParameters': {
'string': 'string'
}
},
]
},
]
},
'EncryptionConfig': {
'RoleArn': 'string',
'KMSKeyArn': 'string'
},
'PredictorExecutionDetails': {
'PredictorExecutions': [
{
'AlgorithmArn': 'string',
'TestWindows': [
{
'TestWindowStart': datetime(2015, 1, 1),
'TestWindowEnd': datetime(2015, 1, 1),
'Status': 'string',
'Message': 'string'
},
]
},
]
},
'EstimatedTimeRemainingInMinutes': 123,
'IsAutoPredictor': True|False,
'DatasetImportJobArns': [
'string',
],
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1),
'OptimizationMetric': 'WAPE'|'RMSE'|'AverageWeightedQuantileLoss'|'MASE'|'MAPE'
}
Response Structure
The ARN of the predictor.
The name of the predictor.
The Amazon Resource Name (ARN) of the algorithm used for model training.
When PerformAutoML is specified, the ARN of the chosen algorithm.
The number of time-steps of the forecast. 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"]
Whether the predictor is set to perform AutoML.
Note
The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS 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.
Whether the predictor is set to perform hyperparameter optimization (HPO).
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 .
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.
The number of times to split the input data. The default is 1. Valid values are 1 through 5.
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
The hyperparameter override values for the algorithm.
Specifies the ranges of valid values for the hyperparameters.
Specifies the tunable range for each categorical hyperparameter.
Specifies a categorical hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
The name of the categorical hyperparameter to tune.
A list of the tunable categories for the hyperparameter.
Specifies the tunable range for each continuous hyperparameter.
Specifies a continuous hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
The name of the hyperparameter to tune.
The maximum tunable value of the hyperparameter.
The minimum tunable value of the hyperparameter.
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:
Specifies the tunable range for each integer hyperparameter.
Specifies an integer hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
The name of the hyperparameter to tune.
The maximum tunable value of the hyperparameter.
The minimum tunable value of the hyperparameter.
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:
Describes the dataset group that contains the data to use to train the predictor.
The Amazon Resource Name (ARN) of the dataset group.
An array of supplementary features. The only supported feature is a holiday calendar.
Note
This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor , see AdditionalDataset .
Describes a supplementary feature of a dataset group. This object is part of the InputDataConfig object. Forecast supports the Weather Index and Holidays built-in featurizations.
Weather Index
The Amazon Forecast Weather Index is a built-in featurization 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 featurization that incorporates a feature-engineered dataset of 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 feature. Valid values: "holiday" and "weather" .
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:
The featurization configuration.
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.
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.
An array of featurization (transformation) information for the fields of a dataset.
Note
This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor , see AttributeConfig .
Provides featurization (transformation) information for a dataset field. This object is part of the FeaturizationConfig object.
For example:
{"AttributeName": "demand",
FeaturizationPipeline [ {
"FeaturizationMethodName": "filling",
"FeaturizationMethodParameters": {"aggregation": "avg", "backfill": "nan"}
} ]
}
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 .
An array of one FeaturizationMethod object that specifies the feature transformation method.
Provides information about the method that featurizes (transforms) a dataset field. The method is part of the FeaturizationPipeline of the Featurization object.
The following is an example of how you specify a FeaturizationMethod object.
{"FeaturizationMethodName": "filling",
"FeaturizationMethodParameters": {"aggregation": "sum", "middlefill": "zero", "backfill": "zero"}
}
The name of the method. The "filling" method is the only supported method.
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.
The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):
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" .
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.
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.
The Amazon Resource Name (ARN) of the KMS key.
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.
An array of the backtests performed to evaluate the accuracy of the predictor against a particular algorithm. The NumberOfBacktestWindows from the object determines the number of windows in the array.
The algorithm used to perform a backtest and the status of those tests.
The ARN of the algorithm used to test the predictor.
An array of test windows used to evaluate the algorithm. The NumberOfBacktestWindows from the object determines the number of windows in the array.
The status, start time, and end time of a backtest, as well as a failure reason if applicable.
The time at which the test began.
The time at which the test ended.
The status of the test. Possible status values are:
If the test failed, the reason why it failed.
The estimated time remaining in minutes for the predictor training job to complete.
Whether the predictor was created with CreateAutoPredictor .
An array of the ARNs of the dataset import jobs used to import training data for the predictor.
The status of the predictor. States include:
Note
The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.
If an error occurred, an informational message about the error.
When the model training task was created.
The last time the resource was modified. The timestamp depends on the status of the job:
The accuracy metric used to optimize the predictor.
Exceptions
Describes a predictor backtest export job created using the CreatePredictorBacktestExportJob operation.
In addition to listing the properties provided by the user in the CreatePredictorBacktestExportJob request, this operation lists the following properties:
See also: AWS API Documentation
Request Syntax
response = client.describe_predictor_backtest_export_job(
PredictorBacktestExportJobArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the predictor backtest export job.
{
'PredictorBacktestExportJobArn': 'string',
'PredictorBacktestExportJobName': 'string',
'PredictorArn': 'string',
'Destination': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Message': 'string',
'Status': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
}
Response Structure
The Amazon Resource Name (ARN) of the predictor backtest export job.
The name of the predictor backtest export job.
The Amazon Resource Name (ARN) of the predictor.
The destination for an export job. Provide an S3 path, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an AWS Key Management Service (KMS) key (optional).
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Information about any errors that may have occurred during the backtest export.
The status of the predictor backtest export job. States include:
When the predictor backtest export job was created.
The last time the resource was modified. The timestamp depends on the status of the job:
Exceptions
Provides metrics on the accuracy of the models that were trained by the CreatePredictor operation. Use metrics to see how well the model performed and to decide whether to use the predictor to generate a forecast. For more information, see Predictor Metrics .
This operation generates metrics for each backtest window that was evaluated. The number of backtest windows (NumberOfBacktestWindows ) is specified using the EvaluationParameters object, which is optionally included in the CreatePredictor request. If NumberOfBacktestWindows isn't specified, the number defaults to one.
The parameters of the filling method determine which items contribute to the metrics. If you want all items to contribute, specify zero . If you want only those items that have complete data in the range being evaluated to contribute, specify nan . For more information, see FeaturizationMethod .
Note
Before you can get accuracy metrics, 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
Request Syntax
response = client.get_accuracy_metrics(
PredictorArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the predictor to get metrics for.
{
'PredictorEvaluationResults': [
{
'AlgorithmArn': 'string',
'TestWindows': [
{
'TestWindowStart': datetime(2015, 1, 1),
'TestWindowEnd': datetime(2015, 1, 1),
'ItemCount': 123,
'EvaluationType': 'SUMMARY'|'COMPUTED',
'Metrics': {
'RMSE': 123.0,
'WeightedQuantileLosses': [
{
'Quantile': 123.0,
'LossValue': 123.0
},
],
'ErrorMetrics': [
{
'ForecastType': 'string',
'WAPE': 123.0,
'RMSE': 123.0,
'MASE': 123.0,
'MAPE': 123.0
},
],
'AverageWeightedQuantileLoss': 123.0
}
},
]
},
],
'IsAutoPredictor': True|False,
'AutoMLOverrideStrategy': 'LatencyOptimized'|'AccuracyOptimized',
'OptimizationMetric': 'WAPE'|'RMSE'|'AverageWeightedQuantileLoss'|'MASE'|'MAPE'
}
Response Structure
An array of results from evaluating the predictor.
The results of evaluating an algorithm. Returned as part of the GetAccuracyMetrics response.
The Amazon Resource Name (ARN) of the algorithm that was evaluated.
The array of test windows used for evaluating the algorithm. The NumberOfBacktestWindows from the EvaluationParameters object determines the number of windows in the array.
The metrics for a time range within the evaluation portion of a dataset. This object is part of the EvaluationResult object.
The TestWindowStart and TestWindowEnd parameters are determined by the BackTestWindowOffset parameter of the EvaluationParameters object.
The timestamp that defines the start of the window.
The timestamp that defines the end of the window.
The number of data points within the window.
The type of evaluation.
Provides metrics used to evaluate the performance of a predictor.
The root-mean-square error (RMSE).
An array of weighted quantile losses. Quantiles divide a probability distribution into regions of equal probability. The distribution in this case is the loss function.
The weighted loss value for a quantile. This object is part of the Metrics object.
The quantile. Quantiles divide a probability distribution into regions of equal probability. For example, if the distribution was divided into 5 regions of equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8.
The difference between the predicted value and the actual value over the quantile, weighted (normalized) by dividing by the sum over all quantiles.
Provides detailed error metrics for each forecast type. Metrics include root-mean square-error (RMSE), mean absolute percentage error (MAPE), mean absolute scaled error (MASE), and weighted average percentage error (WAPE).
Provides detailed error metrics to evaluate the performance of a predictor. This object is part of the Metrics object.
The Forecast type used to compute WAPE, MAPE, MASE, and RMSE.
The weighted absolute percentage error (WAPE).
The root-mean-square error (RMSE).
The Mean Absolute Scaled Error (MASE)
The Mean Absolute Percentage Error (MAPE)
The average value of all weighted quantile losses.
Whether the predictor was created with CreateAutoPredictor .
Note
The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS 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.
The accuracy metric used to optimize the predictor.
Exceptions
Create a paginator for an operation.
Returns an object that can wait for some condition.
Returns a list of dataset groups created using the CreateDatasetGroup operation. For each dataset group, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the dataset group ARN with the DescribeDatasetGroup operation.
See also: AWS API Documentation
Request Syntax
response = client.list_dataset_groups(
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'DatasetGroups': [
{
'DatasetGroupArn': 'string',
'DatasetGroupName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
DatasetGroups (list) --
An array of objects that summarize each dataset group's properties.
(dict) --
Provides a summary of the dataset group properties used in the ListDatasetGroups operation. To get the complete set of properties, call the DescribeDatasetGroup operation, and provide the DatasetGroupArn .
DatasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group.
DatasetGroupName (string) --
The name of the dataset group.
CreationTime (datetime) --
When the dataset group was created.
LastModificationTime (datetime) --
When the dataset group was created or last updated from a call to the UpdateDatasetGroup operation. While the dataset group is being updated, LastModificationTime is the current time of the ListDatasetGroups call.
NextToken (string) --
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
Exceptions
Returns a list of dataset import jobs created using the CreateDatasetImportJob operation. For each import job, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the ARN with the DescribeDatasetImportJob operation. You can filter the list by providing an array of Filter objects.
See also: AWS API Documentation
Request Syntax
response = client.list_dataset_import_jobs(
NextToken='string',
MaxResults=123,
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
]
)
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the datasets that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
For example, to list all dataset import jobs whose status is ACTIVE, you specify the following filter:
"Filters": [ { "Condition": "IS", "Key": "Status", "Value": "ACTIVE" } ]
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
dict
Response Syntax
{
'DatasetImportJobs': [
{
'DatasetImportJobArn': 'string',
'DatasetImportJobName': 'string',
'DataSource': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
DatasetImportJobs (list) --
An array of objects that summarize each dataset import job's properties.
(dict) --
Provides a summary of the dataset import job properties used in the ListDatasetImportJobs operation. To get the complete set of properties, call the DescribeDatasetImportJob operation, and provide the DatasetImportJobArn .
DatasetImportJobArn (string) --
The Amazon Resource Name (ARN) of the dataset import job.
DatasetImportJobName (string) --
The name of the dataset import job.
DataSource (dict) --
The location of the training data to import and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data. The training data must be stored in an Amazon S3 bucket.
If encryption is used, DataSource includes an AWS Key Management Service (KMS) key.
S3Config (dict) --
The path to the data stored in an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the data.
Path (string) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
RoleArn (string) --
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
KMSKeyArn (string) --
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Status (string) --
The status of the dataset import job. States include:
Message (string) --
If an error occurred, an informational message about the error.
CreationTime (datetime) --
When the dataset import job was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
NextToken (string) --
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
Exceptions
Returns a list of datasets created using the CreateDataset operation. For each dataset, a summary of its properties, including its Amazon Resource Name (ARN), is returned. To retrieve the complete set of properties, use the ARN with the DescribeDataset operation.
See also: AWS API Documentation
Request Syntax
response = client.list_datasets(
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'Datasets': [
{
'DatasetArn': 'string',
'DatasetName': 'string',
'DatasetType': 'TARGET_TIME_SERIES'|'RELATED_TIME_SERIES'|'ITEM_METADATA',
'Domain': 'RETAIL'|'CUSTOM'|'INVENTORY_PLANNING'|'EC2_CAPACITY'|'WORK_FORCE'|'WEB_TRAFFIC'|'METRICS',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Datasets (list) --
An array of objects that summarize each dataset's properties.
(dict) --
Provides a summary of the dataset properties used in the ListDatasets operation. To get the complete set of properties, call the DescribeDataset operation, and provide the DatasetArn .
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset.
DatasetName (string) --
The name of the dataset.
DatasetType (string) --
The dataset type.
Domain (string) --
The domain associated with the dataset.
CreationTime (datetime) --
When the dataset was created.
LastModificationTime (datetime) --
When you create a dataset, LastModificationTime is the same as CreationTime . While data is being imported to the dataset, LastModificationTime is the current time of the ListDatasets call. After a CreateDatasetImportJob operation has finished, LastModificationTime is when the import job completed or failed.
NextToken (string) --
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
Exceptions
Returns a list of Explainability resources created using the CreateExplainability operation. This operation returns a summary for each Explainability. You can filter the list using an array of Filter objects.
To retrieve the complete set of properties for a particular Explainability resource, use the ARN with the DescribeExplainability operation.
See also: AWS API Documentation
Request Syntax
response = client.list_explainabilities(
NextToken='string',
MaxResults=123,
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
]
)
An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the resources that match the statement from the list. The match statement consists of a key and a value.
Filter properties
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
dict
Response Syntax
{
'Explainabilities': [
{
'ExplainabilityArn': 'string',
'ExplainabilityName': 'string',
'ResourceArn': 'string',
'ExplainabilityConfig': {
'TimeSeriesGranularity': 'ALL'|'SPECIFIC',
'TimePointGranularity': 'ALL'|'SPECIFIC'
},
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Explainabilities (list) --
An array of objects that summarize the properties of each Explainability resource.
(dict) --
Provides a summary of the Explainability properties used in the ListExplainabilities operation. To get a complete set of properties, call the DescribeExplainability operation, and provide the listed ExplainabilityArn .
ExplainabilityArn (string) --
The Amazon Resource Name (ARN) of the Explainability.
ExplainabilityName (string) --
The name of the Explainability.
ResourceArn (string) --
The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability.
ExplainabilityConfig (dict) --
The configuration settings that define the granularity of time series and time points for the Explainability.
TimeSeriesGranularity (string) --
To create an Explainability for all time series in your datasets, use ALL . To create an Explainability for specific time series in your datasets, use SPECIFIC .
Specify time series by uploading a CSV file to an Amazon S3 bucket and set the location within the DataDestination data type.
TimePointGranularity (string) --
To create an Explainability for all time points in your forecast horizon, use ALL . To create an Explainability for specific time points in your forecast horizon, use SPECIFIC .
Specify time points with the StartDateTime and EndDateTime parameters within the CreateExplainability operation.
Status (string) --
The status of the Explainability. States include:
Message (string) --
Information about any errors that may have occurred during the Explainability creation process.
CreationTime (datetime) --
When the Explainability was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
NextToken (string) --
Returns this token if the response is truncated. To retrieve the next set of results, use the token in the next request.
Exceptions
Returns a list of Explainability exports created using the CreateExplainabilityExport operation. This operation returns a summary for each Explainability export. You can filter the list using an array of Filter objects.
To retrieve the complete set of properties for a particular Explainability export, use the ARN with the DescribeExplainability operation.
See also: AWS API Documentation
Request Syntax
response = client.list_explainability_exports(
NextToken='string',
MaxResults=123,
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
]
)
An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude resources that match the statement from the list. The match statement consists of a key and a value.
Filter properties
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
dict
Response Syntax
{
'ExplainabilityExports': [
{
'ExplainabilityExportArn': 'string',
'ExplainabilityExportName': 'string',
'Destination': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ExplainabilityExports (list) --
An array of objects that summarize the properties of each Explainability export.
(dict) --
Provides a summary of the Explainability export properties used in the ListExplainabilityExports operation. To get a complete set of properties, call the DescribeExplainabilityExport operation, and provide the ExplainabilityExportArn .
ExplainabilityExportArn (string) --
The Amazon Resource Name (ARN) of the Explainability export.
ExplainabilityExportName (string) --
The name of the Explainability export
Destination (dict) --
The destination for an export job. Provide an S3 path, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an AWS Key Management Service (KMS) key (optional).
S3Config (dict) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
Path (string) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
RoleArn (string) --
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
KMSKeyArn (string) --
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Status (string) --
The status of the Explainability export. States include:
Message (string) --
Information about any errors that may have occurred during the Explainability export.
CreationTime (datetime) --
When the Explainability was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
NextToken (string) --
Returns this token if the response is truncated. To retrieve the next set of results, use the token in the next request.
Exceptions
Returns a list of forecast export jobs created using the CreateForecastExportJob operation. For each forecast export job, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). To retrieve the complete set of properties, use the ARN with the DescribeForecastExportJob operation. You can filter the list using an array of Filter objects.
See also: AWS API Documentation
Request Syntax
response = client.list_forecast_export_jobs(
NextToken='string',
MaxResults=123,
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
]
)
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the forecast export jobs that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
For example, to list all jobs that export a forecast named electricityforecast , specify the following filter:
"Filters": [ { "Condition": "IS", "Key": "ForecastArn", "Value": "arn:aws:forecast:us-west-2:<acct-id>:forecast/electricityforecast" } ]
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
dict
Response Syntax
{
'ForecastExportJobs': [
{
'ForecastExportJobArn': 'string',
'ForecastExportJobName': 'string',
'Destination': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ForecastExportJobs (list) --
An array of objects that summarize each export job's properties.
(dict) --
Provides a summary of the forecast export job properties used in the ListForecastExportJobs operation. To get the complete set of properties, call the DescribeForecastExportJob operation, and provide the listed ForecastExportJobArn .
ForecastExportJobArn (string) --
The Amazon Resource Name (ARN) of the forecast export job.
ForecastExportJobName (string) --
The name of the forecast export job.
Destination (dict) --
The path to the Amazon Simple Storage Service (Amazon S3) bucket where the forecast is exported.
S3Config (dict) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
Path (string) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
RoleArn (string) --
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
KMSKeyArn (string) --
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Status (string) --
The status of the forecast export job. States include:
Note
The Status of the forecast export job must be ACTIVE before you can access the forecast in your S3 bucket.
Message (string) --
If an error occurred, an informational message about the error.
CreationTime (datetime) --
When the forecast export job was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
NextToken (string) --
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
Exceptions
Returns a list of forecasts created using the CreateForecast operation. For each forecast, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). To retrieve the complete set of properties, specify the ARN with the DescribeForecast operation. You can filter the list using an array of Filter objects.
See also: AWS API Documentation
Request Syntax
response = client.list_forecasts(
NextToken='string',
MaxResults=123,
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
]
)
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the forecasts that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
For example, to list all forecasts whose status is not ACTIVE, you would specify:
"Filters": [ { "Condition": "IS_NOT", "Key": "Status", "Value": "ACTIVE" } ]
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
dict
Response Syntax
{
'Forecasts': [
{
'ForecastArn': 'string',
'ForecastName': 'string',
'PredictorArn': 'string',
'CreatedUsingAutoPredictor': True|False,
'DatasetGroupArn': 'string',
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Forecasts (list) --
An array of objects that summarize each forecast's properties.
(dict) --
Provides a summary of the forecast properties used in the ListForecasts operation. To get the complete set of properties, call the DescribeForecast operation, and provide the ForecastArn that is listed in the summary.
ForecastArn (string) --
The ARN of the forecast.
ForecastName (string) --
The name of the forecast.
PredictorArn (string) --
The ARN of the predictor used to generate the forecast.
CreatedUsingAutoPredictor (boolean) --
Whether the Forecast was created from an AutoPredictor.
DatasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group that provided the data used to train the predictor.
Status (string) --
The status of the forecast. States include:
Note
The Status of the forecast must be ACTIVE before you can query or export the forecast.
Message (string) --
If an error occurred, an informational message about the error.
CreationTime (datetime) --
When the forecast creation task was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
NextToken (string) --
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
Exceptions
Returns a list of predictor backtest export jobs created using the CreatePredictorBacktestExportJob operation. This operation returns a summary for each backtest export job. You can filter the list using an array of Filter objects.
To retrieve the complete set of properties for a particular backtest export job, use the ARN with the DescribePredictorBacktestExportJob operation.
See also: AWS API Documentation
Request Syntax
response = client.list_predictor_backtest_export_jobs(
NextToken='string',
MaxResults=123,
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
]
)
An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the predictor backtest export jobs that match the statement from the list. The match statement consists of a key and a value.
Filter properties
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
dict
Response Syntax
{
'PredictorBacktestExportJobs': [
{
'PredictorBacktestExportJobArn': 'string',
'PredictorBacktestExportJobName': 'string',
'Destination': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
PredictorBacktestExportJobs (list) --
An array of objects that summarize the properties of each predictor backtest export job.
(dict) --
Provides a summary of the predictor backtest export job properties used in the ListPredictorBacktestExportJobs operation. To get a complete set of properties, call the DescribePredictorBacktestExportJob operation, and provide the listed PredictorBacktestExportJobArn .
PredictorBacktestExportJobArn (string) --
The Amazon Resource Name (ARN) of the predictor backtest export job.
PredictorBacktestExportJobName (string) --
The name of the predictor backtest export job.
Destination (dict) --
The destination for an export job. Provide an S3 path, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an AWS Key Management Service (KMS) key (optional).
S3Config (dict) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
Path (string) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
RoleArn (string) --
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
KMSKeyArn (string) --
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Status (string) --
The status of the predictor backtest export job. States include:
Message (string) --
Information about any errors that may have occurred during the backtest export.
CreationTime (datetime) --
When the predictor backtest export job was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
NextToken (string) --
Returns this token if the response is truncated. To retrieve the next set of results, use the token in the next request.
Exceptions
Returns a list of predictors created using the CreateAutoPredictor or CreatePredictor operations. For each predictor, this operation returns a summary of its properties, including its Amazon Resource Name (ARN).
You can retrieve the complete set of properties by using the ARN with the DescribeAutoPredictor and DescribePredictor operations. You can filter the list using an array of Filter objects.
See also: AWS API Documentation
Request Syntax
response = client.list_predictors(
NextToken='string',
MaxResults=123,
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
]
)
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the predictors that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
For example, to list all predictors whose status is ACTIVE, you would specify:
"Filters": [ { "Condition": "IS", "Key": "Status", "Value": "ACTIVE" } ]
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
dict
Response Syntax
{
'Predictors': [
{
'PredictorArn': 'string',
'PredictorName': 'string',
'DatasetGroupArn': 'string',
'IsAutoPredictor': True|False,
'ReferencePredictorSummary': {
'Arn': 'string',
'State': 'Active'|'Deleted'
},
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Predictors (list) --
An array of objects that summarize each predictor's properties.
(dict) --
Provides a summary of the predictor properties that are used in the ListPredictors operation. To get the complete set of properties, call the DescribePredictor operation, and provide the listed PredictorArn .
PredictorArn (string) --
The ARN of the predictor.
PredictorName (string) --
The name of the predictor.
DatasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group that contains the data used to train the predictor.
IsAutoPredictor (boolean) --
Whether AutoPredictor was used to create the predictor.
ReferencePredictorSummary (dict) --
A summary of the reference predictor used if the predictor was retrained or upgraded.
Arn (string) --
The ARN of the reference predictor.
State (string) --
Whether the reference predictor is Active or Deleted .
Status (string) --
The status of the predictor. States include:
Note
The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.
Message (string) --
If an error occurred, an informational message about the error.
CreationTime (datetime) --
When the model training task was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
NextToken (string) --
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
Exceptions
Lists the tags for an Amazon Forecast resource.
See also: AWS API Documentation
Request Syntax
response = client.list_tags_for_resource(
ResourceArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) that identifies the resource for which to list the tags.
{
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
Response Structure
The tags for the resource.
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
Exceptions
Stops a resource.
The resource undergoes the following states: CREATE_STOPPING and CREATE_STOPPED . You cannot resume a resource once it has been stopped.
This operation can be applied to the following resources (and their corresponding child resources):
See also: AWS API Documentation
Request Syntax
response = client.stop_resource(
ResourceArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) that identifies the resource to stop. The supported ARNs are DatasetImportJobArn , PredictorArn , PredictorBacktestExportJobArn , ForecastArn , ForecastExportJobArn , ExplainabilityArn , and ExplainabilityExportArn .
Exceptions
Associates the specified tags to a resource with the specified resourceArn . If existing tags on a resource are not specified in the request parameters, they are not changed. When a resource is deleted, the tags associated with that resource are also deleted.
See also: AWS API Documentation
Request Syntax
response = client.tag_resource(
ResourceArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The Amazon Resource Name (ARN) that identifies the resource for which to list the tags.
[REQUIRED]
The tags to add to the resource. A tag is an array of key-value pairs.
The following basic restrictions apply to tags:
The optional metadata that you apply to a resource 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:
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{}
Response Structure
Exceptions
Deletes the specified tags from a resource.
See also: AWS API Documentation
Request Syntax
response = client.untag_resource(
ResourceArn='string',
TagKeys=[
'string',
]
)
[REQUIRED]
The Amazon Resource Name (ARN) that identifies the resource for which to list the tags.
[REQUIRED]
The keys of the tags to be removed.
dict
Response Syntax
{}
Response Structure
Exceptions
Replaces the datasets in a dataset group with the specified datasets.
Note
The Status of the dataset group must be ACTIVE before you can use the dataset group to create a predictor. Use the DescribeDatasetGroup operation to get the status.
See also: AWS API Documentation
Request Syntax
response = client.update_dataset_group(
DatasetGroupArn='string',
DatasetArns=[
'string',
]
)
[REQUIRED]
The ARN of the dataset group.
[REQUIRED]
An array of the Amazon Resource Names (ARNs) of the datasets to add to the dataset group.
dict
Response Syntax
{}
Response Structure
Exceptions
The available paginators are:
paginator = client.get_paginator('list_dataset_groups')
Creates an iterator that will paginate through responses from ForecastService.Client.list_dataset_groups().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
{
'DatasetGroups': [
{
'DatasetGroupArn': 'string',
'DatasetGroupName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
An array of objects that summarize each dataset group's properties.
Provides a summary of the dataset group properties used in the ListDatasetGroups operation. To get the complete set of properties, call the DescribeDatasetGroup operation, and provide the DatasetGroupArn .
The Amazon Resource Name (ARN) of the dataset group.
The name of the dataset group.
When the dataset group was created.
When the dataset group was created or last updated from a call to the UpdateDatasetGroup operation. While the dataset group is being updated, LastModificationTime is the current time of the ListDatasetGroups call.
paginator = client.get_paginator('list_dataset_import_jobs')
Creates an iterator that will paginate through responses from ForecastService.Client.list_dataset_import_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
],
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the datasets that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
For example, to list all dataset import jobs whose status is ACTIVE, you specify the following filter:
"Filters": [ { "Condition": "IS", "Key": "Status", "Value": "ACTIVE" } ]
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'DatasetImportJobs': [
{
'DatasetImportJobArn': 'string',
'DatasetImportJobName': 'string',
'DataSource': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
DatasetImportJobs (list) --
An array of objects that summarize each dataset import job's properties.
(dict) --
Provides a summary of the dataset import job properties used in the ListDatasetImportJobs operation. To get the complete set of properties, call the DescribeDatasetImportJob operation, and provide the DatasetImportJobArn .
DatasetImportJobArn (string) --
The Amazon Resource Name (ARN) of the dataset import job.
DatasetImportJobName (string) --
The name of the dataset import job.
DataSource (dict) --
The location of the training data to import and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data. The training data must be stored in an Amazon S3 bucket.
If encryption is used, DataSource includes an AWS Key Management Service (KMS) key.
S3Config (dict) --
The path to the data stored in an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the data.
Path (string) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
RoleArn (string) --
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
KMSKeyArn (string) --
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Status (string) --
The status of the dataset import job. States include:
Message (string) --
If an error occurred, an informational message about the error.
CreationTime (datetime) --
When the dataset import job was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
paginator = client.get_paginator('list_datasets')
Creates an iterator that will paginate through responses from ForecastService.Client.list_datasets().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
{
'Datasets': [
{
'DatasetArn': 'string',
'DatasetName': 'string',
'DatasetType': 'TARGET_TIME_SERIES'|'RELATED_TIME_SERIES'|'ITEM_METADATA',
'Domain': 'RETAIL'|'CUSTOM'|'INVENTORY_PLANNING'|'EC2_CAPACITY'|'WORK_FORCE'|'WEB_TRAFFIC'|'METRICS',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
An array of objects that summarize each dataset's properties.
Provides a summary of the dataset properties used in the ListDatasets operation. To get the complete set of properties, call the DescribeDataset operation, and provide the DatasetArn .
The Amazon Resource Name (ARN) of the dataset.
The name of the dataset.
The dataset type.
The domain associated with the dataset.
When the dataset was created.
When you create a dataset, LastModificationTime is the same as CreationTime . While data is being imported to the dataset, LastModificationTime is the current time of the ListDatasets call. After a CreateDatasetImportJob operation has finished, LastModificationTime is when the import job completed or failed.
paginator = client.get_paginator('list_forecast_export_jobs')
Creates an iterator that will paginate through responses from ForecastService.Client.list_forecast_export_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
],
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the forecast export jobs that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
For example, to list all jobs that export a forecast named electricityforecast , specify the following filter:
"Filters": [ { "Condition": "IS", "Key": "ForecastArn", "Value": "arn:aws:forecast:us-west-2:<acct-id>:forecast/electricityforecast" } ]
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ForecastExportJobs': [
{
'ForecastExportJobArn': 'string',
'ForecastExportJobName': 'string',
'Destination': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
ForecastExportJobs (list) --
An array of objects that summarize each export job's properties.
(dict) --
Provides a summary of the forecast export job properties used in the ListForecastExportJobs operation. To get the complete set of properties, call the DescribeForecastExportJob operation, and provide the listed ForecastExportJobArn .
ForecastExportJobArn (string) --
The Amazon Resource Name (ARN) of the forecast export job.
ForecastExportJobName (string) --
The name of the forecast export job.
Destination (dict) --
The path to the Amazon Simple Storage Service (Amazon S3) bucket where the forecast is exported.
S3Config (dict) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
Path (string) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
RoleArn (string) --
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
KMSKeyArn (string) --
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Status (string) --
The status of the forecast export job. States include:
Note
The Status of the forecast export job must be ACTIVE before you can access the forecast in your S3 bucket.
Message (string) --
If an error occurred, an informational message about the error.
CreationTime (datetime) --
When the forecast export job was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
paginator = client.get_paginator('list_forecasts')
Creates an iterator that will paginate through responses from ForecastService.Client.list_forecasts().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
],
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the forecasts that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
For example, to list all forecasts whose status is not ACTIVE, you would specify:
"Filters": [ { "Condition": "IS_NOT", "Key": "Status", "Value": "ACTIVE" } ]
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Forecasts': [
{
'ForecastArn': 'string',
'ForecastName': 'string',
'PredictorArn': 'string',
'CreatedUsingAutoPredictor': True|False,
'DatasetGroupArn': 'string',
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
Forecasts (list) --
An array of objects that summarize each forecast's properties.
(dict) --
Provides a summary of the forecast properties used in the ListForecasts operation. To get the complete set of properties, call the DescribeForecast operation, and provide the ForecastArn that is listed in the summary.
ForecastArn (string) --
The ARN of the forecast.
ForecastName (string) --
The name of the forecast.
PredictorArn (string) --
The ARN of the predictor used to generate the forecast.
CreatedUsingAutoPredictor (boolean) --
Whether the Forecast was created from an AutoPredictor.
DatasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group that provided the data used to train the predictor.
Status (string) --
The status of the forecast. States include:
Note
The Status of the forecast must be ACTIVE before you can query or export the forecast.
Message (string) --
If an error occurred, an informational message about the error.
CreationTime (datetime) --
When the forecast creation task was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
paginator = client.get_paginator('list_predictor_backtest_export_jobs')
Creates an iterator that will paginate through responses from ForecastService.Client.list_predictor_backtest_export_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
],
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the predictor backtest export jobs that match the statement from the list. The match statement consists of a key and a value.
Filter properties
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'PredictorBacktestExportJobs': [
{
'PredictorBacktestExportJobArn': 'string',
'PredictorBacktestExportJobName': 'string',
'Destination': {
'S3Config': {
'Path': 'string',
'RoleArn': 'string',
'KMSKeyArn': 'string'
}
},
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
PredictorBacktestExportJobs (list) --
An array of objects that summarize the properties of each predictor backtest export job.
(dict) --
Provides a summary of the predictor backtest export job properties used in the ListPredictorBacktestExportJobs operation. To get a complete set of properties, call the DescribePredictorBacktestExportJob operation, and provide the listed PredictorBacktestExportJobArn .
PredictorBacktestExportJobArn (string) --
The Amazon Resource Name (ARN) of the predictor backtest export job.
PredictorBacktestExportJobName (string) --
The name of the predictor backtest export job.
Destination (dict) --
The destination for an export job. Provide an S3 path, an AWS Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an AWS Key Management Service (KMS) key (optional).
S3Config (dict) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
Path (string) --
The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
RoleArn (string) --
The ARN of the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the 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.
KMSKeyArn (string) --
The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key.
Status (string) --
The status of the predictor backtest export job. States include:
Message (string) --
Information about any errors that may have occurred during the backtest export.
CreationTime (datetime) --
When the predictor backtest export job was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job:
paginator = client.get_paginator('list_predictors')
Creates an iterator that will paginate through responses from ForecastService.Client.list_predictors().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
Filters=[
{
'Key': 'string',
'Value': 'string',
'Condition': 'IS'|'IS_NOT'
},
],
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the predictors that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
For example, to list all predictors whose status is ACTIVE, you would specify:
"Filters": [ { "Condition": "IS", "Key": "Status", "Value": "ACTIVE" } ]
Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT , which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The name of the parameter to filter on.
The value to match.
The condition to apply. To include the objects that match the statement, specify IS . To exclude matching objects, specify IS_NOT .
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Predictors': [
{
'PredictorArn': 'string',
'PredictorName': 'string',
'DatasetGroupArn': 'string',
'IsAutoPredictor': True|False,
'ReferencePredictorSummary': {
'Arn': 'string',
'State': 'Active'|'Deleted'
},
'Status': 'string',
'Message': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModificationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
Predictors (list) --
An array of objects that summarize each predictor's properties.
(dict) --
Provides a summary of the predictor properties that are used in the ListPredictors operation. To get the complete set of properties, call the DescribePredictor operation, and provide the listed PredictorArn .
PredictorArn (string) --
The ARN of the predictor.
PredictorName (string) --
The name of the predictor.
DatasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group that contains the data used to train the predictor.
IsAutoPredictor (boolean) --
Whether AutoPredictor was used to create the predictor.
ReferencePredictorSummary (dict) --
A summary of the reference predictor used if the predictor was retrained or upgraded.
Arn (string) --
The ARN of the reference predictor.
State (string) --
Whether the reference predictor is Active or Deleted .
Status (string) --
The status of the predictor. States include:
Note
The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.
Message (string) --
If an error occurred, an informational message about the error.
CreationTime (datetime) --
When the model training task was created.
LastModificationTime (datetime) --
The last time the resource was modified. The timestamp depends on the status of the job: