View Source AWS.Forecast (aws-elixir v1.0.4)
Provides APIs for creating and managing Amazon Forecast resources.
Link to this section Summary
Functions
Creates an Amazon Forecast predictor.
Creates an Amazon Forecast dataset.
Creates a dataset group, which holds a collection of related datasets.
Imports your training data to an Amazon Forecast dataset.
Explainability is only available for Forecasts and Predictors generated from an
AutoPredictor (CreateAutoPredictor)
Exports an Explainability resource created by the CreateExplainability
operation.
Creates a forecast for each item in the TARGET_TIME_SERIES dataset that was
used to train the predictor.
Exports a forecast created by the CreateForecast operation to your
Amazon Simple Storage Service (Amazon S3) bucket.
Creates a predictor monitor resource for an existing auto predictor.
This operation creates a legacy predictor that does not include all the predictor functionalities provided by Amazon Forecast.
Exports backtest forecasts and accuracy metrics generated by the
CreateAutoPredictor or CreatePredictor operations.
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.
A what-if forecast is a forecast that is created from a modified version of the baseline forecast.
Exports a forecast created by the CreateWhatIfForecast operation to your
Amazon Simple Storage Service (Amazon S3) bucket.
Deletes an Amazon Forecast dataset that was created using the CreateDataset operation.
Deletes a dataset group created using the CreateDatasetGroup operation.
Deletes a dataset import job created using the CreateDatasetImportJob operation.
Deletes an Explainability resource.
Deletes an Explainability export.
Deletes a forecast created using the CreateForecast operation.
Deletes a forecast export job created using the CreateForecastExportJob
operation.
Deletes a monitor resource.
Deletes a predictor created using the DescribePredictor or CreatePredictor
operations.
Deletes a predictor backtest export job.
Deletes an entire resource tree.
Deletes a what-if analysis created using the CreateWhatIfAnalysis
operation.
Deletes a what-if forecast created using the CreateWhatIfForecast
operation.
Deletes a what-if forecast export created using the CreateWhatIfForecastExport
operation.
Describes a predictor created using the CreateAutoPredictor operation.
Describes an Amazon Forecast dataset created using the CreateDataset operation.
Describes a dataset group created using the CreateDatasetGroup operation.
Describes a dataset import job created using the CreateDatasetImportJob operation.
Describes an Explainability resource created using the CreateExplainability
operation.
Describes an Explainability export created using the
CreateExplainabilityExport operation.
Describes a forecast created using the CreateForecast operation.
Describes a forecast export job created using the CreateForecastExportJob
operation.
Describes a monitor resource.
This operation is only valid for legacy predictors created with CreatePredictor.
Describes a predictor backtest export job created using the
CreatePredictorBacktestExportJob operation.
Describes the what-if analysis created using the CreateWhatIfAnalysis
operation.
Describes the what-if forecast created using the CreateWhatIfForecast
operation.
Describes the what-if forecast export created using the
CreateWhatIfForecastExport operation.
Provides metrics on the accuracy of the models that were trained by the
CreatePredictor operation.
Returns a list of dataset groups created using the CreateDatasetGroup operation.
Returns a list of dataset import jobs created using the CreateDatasetImportJob operation.
Returns a list of datasets created using the CreateDataset operation.
Returns a list of Explainability resources created using the
CreateExplainability operation.
Returns a list of Explainability exports created using the
CreateExplainabilityExport operation.
Returns a list of forecast export jobs created using the
CreateForecastExportJob operation.
Returns a list of forecasts created using the CreateForecast operation.
Returns a list of the monitoring evaluation results and predictor events collected by the monitor resource during different windows of time.
Returns a list of monitors created with the CreateMonitor operation and
CreateAutoPredictor operation.
Returns a list of predictor backtest export jobs created using the
CreatePredictorBacktestExportJob operation.
Returns a list of predictors created using the CreateAutoPredictor or
CreatePredictor operations.
Lists the tags for an Amazon Forecast resource.
Returns a list of what-if analyses created using the CreateWhatIfAnalysis
operation.
Returns a list of what-if forecast exports created using the
CreateWhatIfForecastExport operation.
Returns a list of what-if forecasts created using the CreateWhatIfForecast
operation.
Resumes a stopped monitor resource.
Stops a resource.
Associates the specified tags to a resource with the specified resourceArn.
Deletes the specified tags from a resource.
Replaces the datasets in a dataset group with the specified datasets.
Link to this section Functions
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
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
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 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 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.
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.
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
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
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.
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:
__
where the 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.
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.
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
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.
create_predictor_backtest_export_job(client, input, options \\ [])
View SourceExports 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:
__.csv
The 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.
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.
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:
≈__
The 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.
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 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.
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 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 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 a monitor resource.
You can only delete a monitor resource with a status of ACTIVE,
ACTIVE_STOPPED, CREATE_FAILED, or CREATE_STOPPED.
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.
delete_predictor_backtest_export_job(client, input, options \\ [])
View SourceDeletes a predictor backtest export job.
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 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 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.
Describes a predictor created using the CreateAutoPredictor 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 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 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 an Explainability resource created using the CreateExplainability
operation.
Describes an Explainability export created using the
CreateExplainabilityExport operation.
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 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
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.
describe_predictor_backtest_export_job(client, input, options \\ [])
View SourceDescribes 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 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 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
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.
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 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 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 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 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 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 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 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 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 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 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.
Lists the tags for an Amazon Forecast resource.
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 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 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.
Resumes a stopped monitor resource.
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
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.
Deletes the specified tags from a resource.
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.