Module aws_sdk_forecast::types

Data structures used by operation inputs/outputs.

Modules

• builders
  Builders
• error
  Error types that Amazon Forecast Service can respond with.

Structs

• Action

  Defines the modifications that you are making to an attribute for a what-if forecast. For example, you can use this operation to create a what-if forecast that investigates a 10% off sale on all shoes. To do this, you specify "AttributeName": "shoes", "Operation": "MULTIPLY", and "Value": "0.90". Pair this operation with the TimeSeriesCondition operation within the CreateWhatIfForecastRequest$TimeSeriesTransformations operation to define a subset of attribute items that are modified.

• AdditionalDataset

  Describes an additional dataset. This object is part of the DataConfig object. Forecast supports the Weather Index and Holidays additional datasets.

• AttributeConfig

  Provides information about the method used to transform attributes.

• Baseline

  Metrics you can use as a baseline for comparison purposes. Use these metrics when you interpret monitoring results for an auto predictor.

• BaselineMetric

  An individual metric that you can use for comparison as you evaluate your monitoring results.

• CategoricalParameterRange

  Specifies a categorical hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.

• ContinuousParameterRange

  Specifies a continuous hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.

• DataConfig

  The data configuration for your dataset group and any additional datasets.

• DataDestination

  The destination for an export job. Provide an S3 path, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an Key Management Service (KMS) key (optional).

• DataSource

  The source of your data, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the data and, optionally, an Key Management Service (KMS) key.

• DatasetGroupSummary

  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.

• DatasetImportJobSummary

  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.

• DatasetSummary

  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.

• EncryptionConfig

  An Key Management Service (KMS) key and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key. You can specify this optional object in the CreateDataset and CreatePredictor requests.

• ErrorMetric

  Provides detailed error metrics to evaluate the performance of a predictor. This object is part of the Metrics object.

• EvaluationParameters

  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.

• EvaluationResult

  The results of evaluating an algorithm. Returned as part of the GetAccuracyMetrics response.

• ExplainabilityConfig

  The ExplainabilityConfig data type defines the number of time series and time points included in CreateExplainability.

• ExplainabilityExportSummary

  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.

• ExplainabilityInfo

  Provides information about the Explainability resource.

• ExplainabilitySummary

  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.

• Featurization
• FeaturizationConfig
• FeaturizationMethod

  Provides information about the method that featurizes (transforms) a dataset field. The method is part of the FeaturizationPipeline of the Featurization object.

• Filter

  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.

• ForecastExportJobSummary

  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.

• ForecastSummary

  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.

• HyperParameterTuningJobConfig

  Configuration information for a hyperparameter tuning job. You specify this object in the CreatePredictor request.

• InputDataConfig
• IntegerParameterRange

  Specifies an integer hyperparameter and it's range of tunable values. This object is part of the ParameterRanges object.

• MetricResult

  An individual metric Forecast calculated when monitoring predictor usage. You can compare the value for this metric to the metric's value in the Baseline to see how your predictor's performance is changing.

• Metrics

  Provides metrics that are used to evaluate the performance of a predictor. This object is part of the WindowSummary object.

• MonitorConfig

  The configuration details for the predictor monitor.

• MonitorDataSource

  The source of the data the monitor used during the evaluation.

• MonitorInfo

  Provides information about the monitor resource.

• MonitorSummary

  Provides a summary of the monitor properties used in the ListMonitors operation. To get a complete set of properties, call the DescribeMonitor operation, and provide the listed MonitorArn.

• ParameterRanges

  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.

• PredictorBacktestExportJobSummary

  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.

• PredictorBaseline

  Metrics you can use as a baseline for comparison purposes. Use these metrics when you interpret monitoring results for an auto predictor.

• PredictorEvent

  Provides details about a predictor event, such as a retraining.

• PredictorExecution

  The algorithm used to perform a backtest and the status of those tests.

• PredictorExecutionDetails

  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.

• PredictorMonitorEvaluation

  Describes the results of a monitor evaluation.

• PredictorSummary

  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.

• ReferencePredictorSummary

  Provides a summary of the reference predictor used when retraining or upgrading a predictor.

• S3Config

  The path to the file(s) in an Amazon Simple Storage Service (Amazon S3) bucket, and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the file(s). Optionally, includes an 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.

• Schema

  Defines the fields of a dataset.

• SchemaAttribute

  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.

• Statistics

  Provides statistics for each data field imported into to an Amazon Forecast dataset with the CreateDatasetImportJob operation.

• SupplementaryFeature
• Tag

  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.

• TestWindowSummary

  The status, start time, and end time of a backtest, as well as a failure reason if applicable.

• TimeAlignmentBoundary

  The time boundary Forecast uses to align and aggregate your data to match your forecast frequency. Provide the unit of time and the time boundary as a key value pair. If you don't provide a time boundary, Forecast uses a set of Default Time Boundaries.

• TimeSeriesCondition

  Creates a subset of items within an attribute that are modified. For example, you can use this operation to create a subset of items that cost $5 or less. To do this, you specify "AttributeName": "price", "AttributeValue": "5", and "Condition": "LESS_THAN". Pair this operation with the Action operation within the CreateWhatIfForecastRequest$TimeSeriesTransformations operation to define how the attribute is modified.

• TimeSeriesIdentifiers

  Details about the import file that contains the time series for which you want to create forecasts.

• TimeSeriesReplacementsDataSource

  A replacement dataset is a modified version of the baseline related time series that contains only the values that you want to include in a what-if forecast. The replacement dataset must contain the forecast dimensions and item identifiers in the baseline related time series as well as at least 1 changed time series. This dataset is merged with the baseline related time series to create a transformed dataset that is used for the what-if forecast.

• TimeSeriesSelector

  Defines the set of time series that are used to create the forecasts in a TimeSeriesIdentifiers object.

• TimeSeriesTransformation

  A transformation function is a pair of operations that select and modify the rows in a related time series. You select the rows that you want with a condition operation and you modify the rows with a transformation operation. All conditions are joined with an AND operation, meaning that all conditions must be true for the transformation to be applied. Transformations are applied in the order that they are listed.

• WeightedQuantileLoss

  The weighted loss value for a quantile. This object is part of the Metrics object.

• WhatIfAnalysisSummary

  Provides a summary of the what-if analysis properties used in the ListWhatIfAnalyses operation. To get the complete set of properties, call the DescribeWhatIfAnalysis operation, and provide the WhatIfAnalysisArn that is listed in the summary.

• WhatIfForecastExportSummary

  Provides a summary of the what-if forecast export properties used in the ListWhatIfForecastExports operation. To get the complete set of properties, call the DescribeWhatIfForecastExport operation, and provide the WhatIfForecastExportArn that is listed in the summary.

• WhatIfForecastSummary

  Provides a summary of the what-if forecast properties used in the ListWhatIfForecasts operation. To get the complete set of properties, call the DescribeWhatIfForecast operation, and provide the WhatIfForecastArn that is listed in the summary.

• WindowSummary

  The metrics for a time range within the evaluation portion of a dataset. This object is part of the EvaluationResult object.

Enums

• AttributeType
  When writing a match expression against AttributeType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• AutoMlOverrideStrategy
  When writing a match expression against AutoMlOverrideStrategy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• Condition
  When writing a match expression against Condition, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DatasetType
  When writing a match expression against DatasetType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DayOfWeek
  When writing a match expression against DayOfWeek, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• Domain
  When writing a match expression against Domain, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• EvaluationType
  When writing a match expression against EvaluationType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• FeaturizationMethodName
  When writing a match expression against FeaturizationMethodName, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• FilterConditionString
  When writing a match expression against FilterConditionString, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• ImportMode
  When writing a match expression against ImportMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• Month
  When writing a match expression against Month, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• Operation
  When writing a match expression against Operation, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• OptimizationMetric
  When writing a match expression against OptimizationMetric, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• ScalingType
  When writing a match expression against ScalingType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• State
  When writing a match expression against State, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• TimePointGranularity
  When writing a match expression against TimePointGranularity, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• TimeSeriesGranularity
  When writing a match expression against TimeSeriesGranularity, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.