Module Awso_forecast_syncSource

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

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

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

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

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.

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. For more information about metrics generated by Forecast see Evaluating Predictor Accuracy

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.

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.

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.

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

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

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).

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

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

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

The ExplainabilityConfig data type defines the number of time series and time points included in CreateExplainability. If you provide a predictor ARN for ResourceArn, you must set both TimePointGranularity and TimeSeriesGranularity to “ALL”. When creating Predictor Explainability, Amazon Forecast considers all time series and time points. If you provide a forecast ARN for ResourceArn, you can set TimePointGranularity and TimeSeriesGranularity to either “ALL” or “Specific”.

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.

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.

Defines the fields of a dataset.

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"} } ] }

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

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.

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 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 Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws 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 of aws do not count against your tags per resource limit.

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.

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.

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.

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.

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.

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.

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.

Describes the results of a monitor evaluation.

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.

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 Explainability export properties used in the ListExplainabilityExports operation. To get a complete set of properties, call the DescribeExplainabilityExport operation, and provide the ExplainabilityExportArn.

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.

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.

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 group properties used in the ListDatasetGroups operation. To get the complete set of properties, call the DescribeDatasetGroup operation, and provide the DatasetGroupArn.

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

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.

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

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.

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

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

We can't process the request because it includes an invalid value or a value that exceeds the valid range.

The specified resource is in use.

We can't find a resource with that Amazon Resource Name (ARN). Check the ARN and try again.

The limit on the number of resources per account has been exceeded.

The token is not valid. Tokens expire after 24 hours.

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.

Defines the set of time series that are used to create the forecasts in a TimeSeriesIdentifiers object. The TimeSeriesIdentifiers object needs the following information: DataSource Format Schema

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.

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.

This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor, see AttributeConfig. 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.

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.

This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor, see DataConfig. 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.

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.

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

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

Provides information about the Explainability resource.

Provides information about the monitor resource.

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. For more information about aggregation, see Data Aggregation for Different Forecast Frequencies. For more information setting a custom time boundary, see Specifying a Time Boundary.

There is already a resource with this name. Try again with a different name.

The configuration details for the predictor monitor.

Replaces the datasets in a dataset group with the specified datasets. 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.

Replaces the datasets in a dataset group with the specified datasets. 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.

Deletes the specified tags from a resource.

Deletes the specified tags from a resource.

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.

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.

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): Dataset Import Job Predictor Job Forecast Job Forecast Export Job Predictor Backtest Export Job Explainability Job Explainability Export Job

Resumes a stopped monitor resource.

Returns a list of what-if forecasts created using the CreateWhatIfForecast operation. For each what-if forecast, 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 what-if forecast ARN with the DescribeWhatIfForecast operation.

Returns a list of what-if forecasts created using the CreateWhatIfForecast operation. For each what-if forecast, 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 what-if forecast ARN with the DescribeWhatIfForecast operation.

Returns a list of what-if forecast exports created using the CreateWhatIfForecastExport operation. For each what-if forecast export, 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 what-if forecast export ARN with the DescribeWhatIfForecastExport operation.

Returns a list of what-if forecast exports created using the CreateWhatIfForecastExport operation. For each what-if forecast export, 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 what-if forecast export ARN with the DescribeWhatIfForecastExport operation.

Returns a list of what-if analyses created using the CreateWhatIfAnalysis operation. For each what-if analysis, 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 what-if analysis ARN with the DescribeWhatIfAnalysis operation.

Returns a list of what-if analyses created using the CreateWhatIfAnalysis operation. For each what-if analysis, 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 what-if analysis ARN with the DescribeWhatIfAnalysis operation.

Lists the tags for an Amazon Forecast resource.

Lists the tags for an Amazon Forecast resource.

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.

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.

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.

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.

Returns a list of monitors created with the CreateMonitor operation and CreateAutoPredictor operation. For each monitor resource, this operation returns of a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve a complete set of properties of a monitor resource by specify the monitor's ARN in the DescribeMonitor operation.

Returns a list of monitors created with the CreateMonitor operation and CreateAutoPredictor operation. For each monitor resource, this operation returns of a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve a complete set of properties of a monitor resource by specify the monitor's ARN in the DescribeMonitor operation.

Returns a list of the monitoring evaluation results and predictor events collected by the monitor resource during different windows of time. For information about monitoring see predictor-monitoring. For more information about retrieving monitoring results see Viewing Monitoring Results.

Returns a list of the monitoring evaluation results and predictor events collected by the monitor resource during different windows of time. For information about monitoring see predictor-monitoring. For more information about retrieving monitoring results see Viewing Monitoring Results.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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. 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.

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. 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.

Describes the what-if forecast created using the CreateWhatIfForecast operation. In addition to listing the properties provided in the CreateWhatIfForecast request, this operation lists the following properties: CreationTime LastModificationTime Message - If an error occurred, information about the error. Status

Describes the what-if forecast created using the CreateWhatIfForecast operation. In addition to listing the properties provided in the CreateWhatIfForecast request, this operation lists the following properties: CreationTime LastModificationTime Message - If an error occurred, information about the error. Status

Describes the what-if forecast export created using the CreateWhatIfForecastExport operation. In addition to listing the properties provided in the CreateWhatIfForecastExport request, this operation lists the following properties: CreationTime LastModificationTime Message - If an error occurred, information about the error. Status

Describes the what-if forecast export created using the CreateWhatIfForecastExport operation. In addition to listing the properties provided in the CreateWhatIfForecastExport request, this operation lists the following properties: CreationTime LastModificationTime Message - If an error occurred, information about the error. Status

Describes the what-if analysis created using the CreateWhatIfAnalysis operation. In addition to listing the properties provided in the CreateWhatIfAnalysis request, this operation lists the following properties: CreationTime LastModificationTime Message - If an error occurred, information about the error. Status

Describes the what-if analysis created using the CreateWhatIfAnalysis operation. In addition to listing the properties provided in the CreateWhatIfAnalysis request, this operation lists the following properties: CreationTime LastModificationTime Message - If an error occurred, information about the error. Status

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: DatasetImportJobArns - The dataset import jobs used to import training data. AutoMLAlgorithmArns - If AutoML is performed, the algorithms that were evaluated. CreationTime LastModificationTime Status Message - If an error occurred, information about the error.

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: DatasetImportJobArns - The dataset import jobs used to import training data. AutoMLAlgorithmArns - If AutoML is performed, the algorithms that were evaluated. CreationTime LastModificationTime Status Message - If an error occurred, information about the error.

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: CreationTime LastModificationTime Status Message (if an error occurred)

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: CreationTime LastModificationTime Status Message (if an error occurred)

Describes a monitor resource. In addition to listing the properties provided in the CreateMonitor request, this operation lists the following properties: Baseline CreationTime LastEvaluationTime LastEvaluationState LastModificationTime Message Status

Describes a monitor resource. In addition to listing the properties provided in the CreateMonitor request, this operation lists the following properties: Baseline CreationTime LastEvaluationTime LastEvaluationState LastModificationTime Message Status

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: DatasetGroupArn - The dataset group that provided the training data. CreationTime LastModificationTime Status Message - If an error occurred, information about the error.

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: DatasetGroupArn - The dataset group that provided the training data. CreationTime LastModificationTime Status Message - If an error occurred, information about the error.

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: CreationTime LastModificationTime Status Message - If an error occurred, information about the error.

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: CreationTime LastModificationTime Status Message - If an error occurred, information about the error.

Describes an Explainability resource created using the CreateExplainability operation.

Describes an Explainability resource created using the CreateExplainability operation.

Describes an Explainability export created using the CreateExplainabilityExport operation.

Describes an Explainability export created using the CreateExplainabilityExport operation.

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: CreationTime LastModificationTime Status

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: CreationTime LastModificationTime Status

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: CreationTime LastModificationTime DataSize FieldStatistics Status Message - If an error occurred, information about the error.

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: CreationTime LastModificationTime DataSize FieldStatistics Status Message - If an error occurred, information about the error.

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: DatasetArns - The datasets belonging to the group. CreationTime LastModificationTime Status

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: DatasetArns - The datasets belonging to the group. CreationTime LastModificationTime Status

Describes a predictor created using the CreateAutoPredictor operation.

Describes a predictor created using the CreateAutoPredictor operation.

Deletes a what-if forecast created using the CreateWhatIfForecast operation. You can delete only what-if forecasts that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeWhatIfForecast operation. You can't delete a what-if forecast while it is being exported. After a what-if forecast is deleted, you can no longer query the what-if analysis.

Deletes a what-if forecast export created using the CreateWhatIfForecastExport operation. You can delete only what-if forecast exports that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeWhatIfForecastExport operation.

Deletes a what-if analysis created using the CreateWhatIfAnalysis operation. You can delete only what-if analyses that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeWhatIfAnalysis operation. You can't delete a what-if analysis while any of its forecasts are being exported.

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: Dataset: dataset import jobs Dataset Group: predictors, predictor backtest export jobs, forecasts, forecast export jobs Predictor: predictor backtest export jobs, forecasts, forecast export jobs Forecast: forecast export jobs DeleteResourceTree will only delete Amazon Forecast resources, and will not delete datasets or exported files stored in Amazon S3.

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.

Deletes a predictor backtest export job.

Deletes a monitor resource. You can only delete a monitor resource with a status of ACTIVE, ACTIVE_STOPPED, CREATE_FAILED, or CREATE_STOPPED.

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.

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.

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.

Deletes an Explainability export.

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. Forecast does not automatically update any dataset groups that contain the deleted dataset. In order to update the dataset group, use the UpdateDatasetGroup operation, omitting the deleted dataset's ARN.

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.

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.

A what-if forecast is a forecast that is created from a modified version of the baseline forecast. Each what-if forecast incorporates either a replacement dataset or a set of transformations to the original dataset.

A what-if forecast is a forecast that is created from a modified version of the baseline forecast. Each what-if forecast incorporates either a replacement dataset or a set of transformations to the original dataset.

Exports a forecast created by the CreateWhatIfForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket. The forecast file name will match the following conventions: ≈<ForecastExportJobName>_<ExportTimestamp>_<PartNumber> The <ExportTimestamp> component is in Java SimpleDateFormat (yyyy-MM-ddTHH-mm-ssZ). You must specify a DataDestination object that includes an 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 what-if forecast export jobs, use the ListWhatIfForecastExports operation. 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 DescribeWhatIfForecastExport operation.

Exports a forecast created by the CreateWhatIfForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket. The forecast file name will match the following conventions: ≈<ForecastExportJobName>_<ExportTimestamp>_<PartNumber> The <ExportTimestamp> component is in Java SimpleDateFormat (yyyy-MM-ddTHH-mm-ssZ). You must specify a DataDestination object that includes an 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 what-if forecast export jobs, use the ListWhatIfForecastExports operation. 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 DescribeWhatIfForecastExport operation.

What-if analysis is a scenario modeling technique where you make a hypothetical change to a time series and compare the forecasts generated by these changes against the baseline, unchanged time series. It is important to remember that the purpose of a what-if analysis is to understand how a forecast can change given different modifications to the baseline time series. For example, imagine you are a clothing retailer who is considering an end of season sale to clear space for new styles. After creating a baseline forecast, you can use a what-if analysis to investigate how different sales tactics might affect your goals. You could create a scenario where everything is given a 25% markdown, and another where everything is given a fixed dollar markdown. You could create a scenario where the sale lasts for one week and another where the sale lasts for one month. With a what-if analysis, you can compare many different scenarios against each other. Note that a what-if analysis is meant to display what the forecasting model has learned and how it will behave in the scenarios that you are evaluating. Do not blindly use the results of the what-if analysis to make business decisions. For instance, forecasts might not be accurate for novel scenarios where there is no reference available to determine whether a forecast is good. The TimeSeriesSelector object defines the items that you want in the what-if analysis.

What-if analysis is a scenario modeling technique where you make a hypothetical change to a time series and compare the forecasts generated by these changes against the baseline, unchanged time series. It is important to remember that the purpose of a what-if analysis is to understand how a forecast can change given different modifications to the baseline time series. For example, imagine you are a clothing retailer who is considering an end of season sale to clear space for new styles. After creating a baseline forecast, you can use a what-if analysis to investigate how different sales tactics might affect your goals. You could create a scenario where everything is given a 25% markdown, and another where everything is given a fixed dollar markdown. You could create a scenario where the sale lasts for one week and another where the sale lasts for one month. With a what-if analysis, you can compare many different scenarios against each other. Note that a what-if analysis is meant to display what the forecasting model has learned and how it will behave in the scenarios that you are evaluating. Do not blindly use the results of the what-if analysis to make business decisions. For instance, forecasts might not be accurate for novel scenarios where there is no reference available to determine whether a forecast is good. The TimeSeriesSelector object defines the items that you want in the what-if analysis.

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: AlgorithmArn HPOConfig PerformHPO TrainingParameters To get a list of all of your predictors, use the ListPredictors operation. 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.

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: AlgorithmArn HPOConfig PerformHPO TrainingParameters To get a list of all of your predictors, use the ListPredictors operation. 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.

Exports backtest forecasts and accuracy metrics generated by the CreateAutoPredictor or CreatePredictor operations. Two folders containing CSV or Parquet 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 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. 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.

Exports backtest forecasts and accuracy metrics generated by the CreateAutoPredictor or CreatePredictor operations. Two folders containing CSV or Parquet 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 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. 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.

Creates a predictor monitor resource for an existing auto predictor. Predictor monitoring allows you to see how your predictor's performance changes over time. For more information, see Predictor Monitoring.

Creates a predictor monitor resource for an existing auto predictor. Predictor monitoring allows you to see how your predictor's performance changes over time. For more information, see Predictor Monitoring.

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. 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. The Status of the forecast must be ACTIVE before you can query or export the forecast. Use the DescribeForecast operation to get the status. By default, a forecast includes predictions for every item (item_id) in the dataset group that was used to train the predictor. However, you can use the TimeSeriesSelector object to generate a forecast on a subset of time series. Forecast creation is skipped for any time series that you specify that are not in the input dataset. The forecast export file will not contain these time series or their forecasted values.

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. 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. The Status of the forecast must be ACTIVE before you can query or export the forecast. Use the DescribeForecast operation to get the status. By default, a forecast includes predictions for every item (item_id) in the dataset group that was used to train the predictor. However, you can use the TimeSeriesSelector object to generate a forecast on a subset of time series. Forecast creation is skipped for any time series that you specify that are not in the input dataset. The forecast export file will not contain these time series or their forecasted values.

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 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. 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.

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 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. 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.

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 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: ExplainabilityName - A unique name for the Explainability. ResourceArn - The Arn of the predictor. TimePointGranularity - Must be set to “ALL”. TimeSeriesGranularity - Must be set to “ALL”. Do not specify a value for the following parameters: DataSource - Only valid when TimeSeriesGranularity is “SPECIFIC”. Schema - Only valid when TimeSeriesGranularity is “SPECIFIC”. StartDateTime - Only valid when TimePointGranularity is “SPECIFIC”. EndDateTime - Only valid when TimePointGranularity is “SPECIFIC”. CreateExplainability with a Forecast ARN You can specify a maximum of 50 time series and 500 time points. The following parameters are required when providing a Predictor ARN: ExplainabilityName - A unique name for the Explainability. ResourceArn - The Arn of the forecast. TimePointGranularity - Either “ALL” or “SPECIFIC”. TimeSeriesGranularity - Either “ALL” or “SPECIFIC”. If you set TimeSeriesGranularity to “SPECIFIC”, you must also provide the following: DataSource - The S3 location of the CSV file specifying your time series. Schema - The Schema defines the attributes and attribute types listed in the Data Source. If you set TimePointGranularity to “SPECIFIC”, you must also provide the following: StartDateTime - The first timestamp in the range of time points. EndDateTime - The last timestamp in the range of time points.

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 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: ExplainabilityName - A unique name for the Explainability. ResourceArn - The Arn of the predictor. TimePointGranularity - Must be set to “ALL”. TimeSeriesGranularity - Must be set to “ALL”. Do not specify a value for the following parameters: DataSource - Only valid when TimeSeriesGranularity is “SPECIFIC”. Schema - Only valid when TimeSeriesGranularity is “SPECIFIC”. StartDateTime - Only valid when TimePointGranularity is “SPECIFIC”. EndDateTime - Only valid when TimePointGranularity is “SPECIFIC”. CreateExplainability with a Forecast ARN You can specify a maximum of 50 time series and 500 time points. The following parameters are required when providing a Predictor ARN: ExplainabilityName - A unique name for the Explainability. ResourceArn - The Arn of the forecast. TimePointGranularity - Either “ALL” or “SPECIFIC”. TimeSeriesGranularity - Either “ALL” or “SPECIFIC”. If you set TimeSeriesGranularity to “SPECIFIC”, you must also provide the following: DataSource - The S3 location of the CSV file specifying your time series. Schema - The Schema defines the attributes and attribute types listed in the Data Source. If you set TimePointGranularity to “SPECIFIC”, you must also provide the following: StartDateTime - The first timestamp in the range of time points. EndDateTime - The last timestamp in the range of time points.

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 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. 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.

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 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. 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.

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: DataFrequency - How frequently your historical time-series data is collected. Domain and DatasetType - Each dataset has an associated dataset domain and a type within the domain. Amazon Forecast provides a list of predefined domains and types within each domain. For each unique dataset domain and type within the domain, Amazon Forecast requires your data to include a minimum set of predefined fields. Schema - A schema specifies the fields in the dataset, including the field name and data type. 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 Importing datasets. To get a list of all your datasets, use the ListDatasets operation. For example Forecast datasets, see the Amazon Forecast Sample GitHub repository. The Status of a dataset must be ACTIVE before you can import training data. Use the DescribeDataset operation to get the status.

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: DataFrequency - How frequently your historical time-series data is collected. Domain and DatasetType - Each dataset has an associated dataset domain and a type within the domain. Amazon Forecast provides a list of predefined domains and types within each domain. For each unique dataset domain and type within the domain, Amazon Forecast requires your data to include a minimum set of predefined fields. Schema - A schema specifies the fields in the dataset, including the field name and data type. 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 Importing datasets. To get a list of all your datasets, use the ListDatasets operation. For example Forecast datasets, see the Amazon Forecast Sample GitHub repository. The Status of a dataset must be ACTIVE before you can import training data. Use the DescribeDataset operation to get the status.

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 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 Amazon Web Services system. For more information, see Set up permissions. The training data must be in CSV or Parquet format. The delimiter must be a comma (,). You can specify the path to a specific 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.

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 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 Amazon Web Services system. For more information, see Set up permissions. The training data must be in CSV or Parquet format. The delimiter must be a comma (,). You can specify the path to a specific 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.

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 Dataset groups. To get a list of all your datasets groups, use the ListDatasetGroups operation. 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.

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 Dataset groups. To get a list of all your datasets groups, use the ListDatasetGroups operation. 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.

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

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