Crate rusoto_forecast
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Provides APIs for creating and managing Amazon Forecast resources.
If you’re using the service, you’re probably looking for ForecastClient and Forecast.
Structs
Specifies a categorical hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
Specifies a continuous hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
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 source of your training 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. This object is submitted in the CreateDatasetImportJob request.
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
.
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
.
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
.
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.
Provides detailed error metrics to evaluate the performance of a predictor. This object is part of the Metrics object.
Parameters that define how to split a dataset into training data and testing data, and the number of iterations to perform. These parameters are specified in the predefined algorithms but you can override them in the CreatePredictor request.
The results of evaluating an algorithm. Returned as part of the GetAccuracyMetrics response.
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"}
} ]
}
In a CreatePredictor operation, the specified algorithm trains a model using the specified dataset group. You can optionally tell the operation to modify data fields prior to training a model. These modifications are referred to as featurization.
You define featurization using the FeaturizationConfig
object. You specify an array of transformations, one for each field that you want to featurize. You then include the FeaturizationConfig
object in your CreatePredictor
request. Amazon Forecast applies the featurization to the TARGET_TIME_SERIES
and RELATED_TIME_SERIES
datasets before model training.
You can create multiple featurization configurations. For example, you might call the CreatePredictor
operation twice by specifying different featurization configurations.
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"}
}
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.
A client for the Amazon Forecast Service API.
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
.
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.
Configuration information for a hyperparameter tuning job. You specify this object in the CreatePredictor request.
A hyperparameter is a parameter that governs the model training process. You set hyperparameters before training starts, unlike model parameters, which are determined during training. The values of the hyperparameters effect which values are chosen for the model parameters.
In a hyperparameter tuning job, Amazon Forecast chooses the set of hyperparameter values that optimize a specified metric. Forecast accomplishes this by running many training jobs over a range of hyperparameter values. The optimum set of values depends on the algorithm, the training data, and the specified metric objective.
The data used to train a predictor. The data includes a dataset group and any supplementary features. You specify this object in the CreatePredictor request.
Specifies an integer hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.
Provides metrics that are used to evaluate the performance of a predictor. This object is part of the WindowSummary object.
Specifies the categorical, continuous, and integer hyperparameters, and their ranges of tunable values. The range of tunable values determines which values that a hyperparameter tuning job can choose for the specified hyperparameter. This object is part of the HyperParameterTuningJobConfig object.
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
.
The algorithm used to perform a backtest and the status of those tests.
Contains details on the backtests performed to evaluate the accuracy of the predictor. The tests are returned in descending order of accuracy, with the most accurate backtest appearing first. You specify the number of backtests to perform when you call the operation.
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
.
The path to the file(s) in an Amazon Simple Storage Service (Amazon S3) bucket, and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the file(s). Optionally, includes an AWS Key Management Service (KMS) key. This object is part of the DataSource object that is submitted in the CreateDatasetImportJob request, and part of the DataDestination object.
Defines the fields of a dataset. You specify this object in the CreateDataset request.
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.
Provides statistics for each data field imported into to an Amazon Forecast dataset with the CreateDatasetImportJob operation.
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 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:
-
Maximum number of tags per resource - 50.
-
For each resource, each tag key must be unique, and each tag key can have only one value.
-
Maximum key length - 128 Unicode characters in UTF-8.
-
Maximum value length - 256 Unicode characters in UTF-8.
-
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
-
Tag keys and values are case sensitive.
-
Do not use
aws:
,AWS:
, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value hasaws
as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix ofaws
do not count against your tags per resource limit.
The status, start time, and end time of a backtest, as well as a failure reason if applicable.
The weighted loss value for a quantile. This object is part of the Metrics object.
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.
Enums
Errors returned by CreateDataset
Errors returned by CreateDatasetGroup
Errors returned by CreateDatasetImportJob
Errors returned by CreateForecast
Errors returned by CreateForecastExportJob
Errors returned by CreatePredictorBacktestExportJob
Errors returned by CreatePredictor
Errors returned by DeleteDataset
Errors returned by DeleteDatasetGroup
Errors returned by DeleteDatasetImportJob
Errors returned by DeleteForecast
Errors returned by DeleteForecastExportJob
Errors returned by DeletePredictorBacktestExportJob
Errors returned by DeletePredictor
Errors returned by DeleteResourceTree
Errors returned by DescribeDataset
Errors returned by DescribeDatasetGroup
Errors returned by DescribeDatasetImportJob
Errors returned by DescribeForecast
Errors returned by DescribeForecastExportJob
Errors returned by DescribePredictorBacktestExportJob
Errors returned by DescribePredictor
Errors returned by GetAccuracyMetrics
Errors returned by ListDatasetGroups
Errors returned by ListDatasetImportJobs
Errors returned by ListDatasets
Errors returned by ListForecastExportJobs
Errors returned by ListForecasts
Errors returned by ListPredictorBacktestExportJobs
Errors returned by ListPredictors
Errors returned by ListTagsForResource
Errors returned by StopResource
Errors returned by TagResource
Errors returned by UntagResource
Errors returned by UpdateDatasetGroup
Traits
Trait representing the capabilities of the Amazon Forecast Service API. Amazon Forecast Service clients implement this trait.