PNG  IHDR;IDATxܻn0K )(pA 7LeG{ §㻢|ذaÆ 6lذaÆ 6lذaÆ 6lom$^yذag5bÆ 6lذaÆ 6lذa{ 6lذaÆ `}HFkm,mӪôô! x|'ܢ˟;E:9&ᶒ}{v]n&6 h_tڠ͵-ҫZ;Z$.Pkž)!o>}leQfJTu іچ\X=8Rن4`Vwl>nG^is"ms$ui?wbs[m6K4O.4%/bC%t Mז -lG6mrz2s%9s@-k9=)kB5\+͂Zsٲ Rn~GRC wIcIn7jJhۛNCS|j08yiHKֶۛkɈ+;SzL/F*\Ԕ#"5m2[S=gnaPeғL lذaÆ 6l^ḵaÆ 6lذaÆ 6lذa; _ذaÆ 6lذaÆ 6lذaÆ RIENDB` package Paws::Forecast; use Moose; sub service { 'forecast' } sub signing_name { 'forecast' } sub version { '2018-06-26' } sub target_prefix { 'AmazonForecast' } sub json_version { "1.1" } has max_attempts => (is => 'ro', isa => 'Int', default => 5); has retry => (is => 'ro', isa => 'HashRef', default => sub { { base => 'rand', type => 'exponential', growth_factor => 2 } }); has retriables => (is => 'ro', isa => 'ArrayRef', default => sub { [ ] }); with 'Paws::API::Caller', 'Paws::API::EndpointResolver', 'Paws::Net::V4Signature', 'Paws::Net::JsonCaller'; sub CreateDataset { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::CreateDataset', @_); return $self->caller->do_call($self, $call_object); } sub CreateDatasetGroup { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::CreateDatasetGroup', @_); return $self->caller->do_call($self, $call_object); } sub CreateDatasetImportJob { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::CreateDatasetImportJob', @_); return $self->caller->do_call($self, $call_object); } sub CreateForecast { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::CreateForecast', @_); return $self->caller->do_call($self, $call_object); } sub CreateForecastExportJob { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::CreateForecastExportJob', @_); return $self->caller->do_call($self, $call_object); } sub CreatePredictor { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::CreatePredictor', @_); return $self->caller->do_call($self, $call_object); } sub CreatePredictorBacktestExportJob { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::CreatePredictorBacktestExportJob', @_); return $self->caller->do_call($self, $call_object); } sub DeleteDataset { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DeleteDataset', @_); return $self->caller->do_call($self, $call_object); } sub DeleteDatasetGroup { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DeleteDatasetGroup', @_); return $self->caller->do_call($self, $call_object); } sub DeleteDatasetImportJob { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DeleteDatasetImportJob', @_); return $self->caller->do_call($self, $call_object); } sub DeleteForecast { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DeleteForecast', @_); return $self->caller->do_call($self, $call_object); } sub DeleteForecastExportJob { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DeleteForecastExportJob', @_); return $self->caller->do_call($self, $call_object); } sub DeletePredictor { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DeletePredictor', @_); return $self->caller->do_call($self, $call_object); } sub DeletePredictorBacktestExportJob { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DeletePredictorBacktestExportJob', @_); return $self->caller->do_call($self, $call_object); } sub DeleteResourceTree { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DeleteResourceTree', @_); return $self->caller->do_call($self, $call_object); } sub DescribeDataset { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DescribeDataset', @_); return $self->caller->do_call($self, $call_object); } sub DescribeDatasetGroup { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DescribeDatasetGroup', @_); return $self->caller->do_call($self, $call_object); } sub DescribeDatasetImportJob { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DescribeDatasetImportJob', @_); return $self->caller->do_call($self, $call_object); } sub DescribeForecast { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DescribeForecast', @_); return $self->caller->do_call($self, $call_object); } sub DescribeForecastExportJob { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DescribeForecastExportJob', @_); return $self->caller->do_call($self, $call_object); } sub DescribePredictor { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DescribePredictor', @_); return $self->caller->do_call($self, $call_object); } sub DescribePredictorBacktestExportJob { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::DescribePredictorBacktestExportJob', @_); return $self->caller->do_call($self, $call_object); } sub GetAccuracyMetrics { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::GetAccuracyMetrics', @_); return $self->caller->do_call($self, $call_object); } sub ListDatasetGroups { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::ListDatasetGroups', @_); return $self->caller->do_call($self, $call_object); } sub ListDatasetImportJobs { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::ListDatasetImportJobs', @_); return $self->caller->do_call($self, $call_object); } sub ListDatasets { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::ListDatasets', @_); return $self->caller->do_call($self, $call_object); } sub ListForecastExportJobs { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::ListForecastExportJobs', @_); return $self->caller->do_call($self, $call_object); } sub ListForecasts { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::ListForecasts', @_); return $self->caller->do_call($self, $call_object); } sub ListPredictorBacktestExportJobs { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::ListPredictorBacktestExportJobs', @_); return $self->caller->do_call($self, $call_object); } sub ListPredictors { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::ListPredictors', @_); return $self->caller->do_call($self, $call_object); } sub ListTagsForResource { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::ListTagsForResource', @_); return $self->caller->do_call($self, $call_object); } sub StopResource { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::StopResource', @_); return $self->caller->do_call($self, $call_object); } sub TagResource { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::TagResource', @_); return $self->caller->do_call($self, $call_object); } sub UntagResource { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::UntagResource', @_); return $self->caller->do_call($self, $call_object); } sub UpdateDatasetGroup { my $self = shift; my $call_object = $self->new_with_coercions('Paws::Forecast::UpdateDatasetGroup', @_); return $self->caller->do_call($self, $call_object); } sub ListAllDatasetGroups { my $self = shift; my $callback = shift @_ if (ref($_[0]) eq 'CODE'); my $result = $self->ListDatasetGroups(@_); my $next_result = $result; if (not defined $callback) { while ($next_result->NextToken) { $next_result = $self->ListDatasetGroups(@_, NextToken => $next_result->NextToken); push @{ $result->DatasetGroups }, @{ $next_result->DatasetGroups }; } return $result; } else { while ($result->NextToken) { $callback->($_ => 'DatasetGroups') foreach (@{ $result->DatasetGroups }); $result = $self->ListDatasetGroups(@_, NextToken => $result->NextToken); } $callback->($_ => 'DatasetGroups') foreach (@{ $result->DatasetGroups }); } return undef } sub ListAllDatasetImportJobs { my $self = shift; my $callback = shift @_ if (ref($_[0]) eq 'CODE'); my $result = $self->ListDatasetImportJobs(@_); my $next_result = $result; if (not defined $callback) { while ($next_result->NextToken) { $next_result = $self->ListDatasetImportJobs(@_, NextToken => $next_result->NextToken); push @{ $result->DatasetImportJobs }, @{ $next_result->DatasetImportJobs }; } return $result; } else { while ($result->NextToken) { $callback->($_ => 'DatasetImportJobs') foreach (@{ $result->DatasetImportJobs }); $result = $self->ListDatasetImportJobs(@_, NextToken => $result->NextToken); } $callback->($_ => 'DatasetImportJobs') foreach (@{ $result->DatasetImportJobs }); } return undef } sub ListAllDatasets { my $self = shift; my $callback = shift @_ if (ref($_[0]) eq 'CODE'); my $result = $self->ListDatasets(@_); my $next_result = $result; if (not defined $callback) { while ($next_result->NextToken) { $next_result = $self->ListDatasets(@_, NextToken => $next_result->NextToken); push @{ $result->Datasets }, @{ $next_result->Datasets }; } return $result; } else { while ($result->NextToken) { $callback->($_ => 'Datasets') foreach (@{ $result->Datasets }); $result = $self->ListDatasets(@_, NextToken => $result->NextToken); } $callback->($_ => 'Datasets') foreach (@{ $result->Datasets }); } return undef } sub ListAllForecastExportJobs { my $self = shift; my $callback = shift @_ if (ref($_[0]) eq 'CODE'); my $result = $self->ListForecastExportJobs(@_); my $next_result = $result; if (not defined $callback) { while ($next_result->NextToken) { $next_result = $self->ListForecastExportJobs(@_, NextToken => $next_result->NextToken); push @{ $result->ForecastExportJobs }, @{ $next_result->ForecastExportJobs }; } return $result; } else { while ($result->NextToken) { $callback->($_ => 'ForecastExportJobs') foreach (@{ $result->ForecastExportJobs }); $result = $self->ListForecastExportJobs(@_, NextToken => $result->NextToken); } $callback->($_ => 'ForecastExportJobs') foreach (@{ $result->ForecastExportJobs }); } return undef } sub ListAllForecasts { my $self = shift; my $callback = shift @_ if (ref($_[0]) eq 'CODE'); my $result = $self->ListForecasts(@_); my $next_result = $result; if (not defined $callback) { while ($next_result->NextToken) { $next_result = $self->ListForecasts(@_, NextToken => $next_result->NextToken); push @{ $result->Forecasts }, @{ $next_result->Forecasts }; } return $result; } else { while ($result->NextToken) { $callback->($_ => 'Forecasts') foreach (@{ $result->Forecasts }); $result = $self->ListForecasts(@_, NextToken => $result->NextToken); } $callback->($_ => 'Forecasts') foreach (@{ $result->Forecasts }); } return undef } sub ListAllPredictorBacktestExportJobs { my $self = shift; my $callback = shift @_ if (ref($_[0]) eq 'CODE'); my $result = $self->ListPredictorBacktestExportJobs(@_); my $next_result = $result; if (not defined $callback) { while ($next_result->NextToken) { $next_result = $self->ListPredictorBacktestExportJobs(@_, NextToken => $next_result->NextToken); push @{ $result->PredictorBacktestExportJobs }, @{ $next_result->PredictorBacktestExportJobs }; } return $result; } else { while ($result->NextToken) { $callback->($_ => 'PredictorBacktestExportJobs') foreach (@{ $result->PredictorBacktestExportJobs }); $result = $self->ListPredictorBacktestExportJobs(@_, NextToken => $result->NextToken); } $callback->($_ => 'PredictorBacktestExportJobs') foreach (@{ $result->PredictorBacktestExportJobs }); } return undef } sub ListAllPredictors { my $self = shift; my $callback = shift @_ if (ref($_[0]) eq 'CODE'); my $result = $self->ListPredictors(@_); my $next_result = $result; if (not defined $callback) { while ($next_result->NextToken) { $next_result = $self->ListPredictors(@_, NextToken => $next_result->NextToken); push @{ $result->Predictors }, @{ $next_result->Predictors }; } return $result; } else { while ($result->NextToken) { $callback->($_ => 'Predictors') foreach (@{ $result->Predictors }); $result = $self->ListPredictors(@_, NextToken => $result->NextToken); } $callback->($_ => 'Predictors') foreach (@{ $result->Predictors }); } return undef } sub operations { qw/CreateDataset CreateDatasetGroup CreateDatasetImportJob CreateForecast CreateForecastExportJob CreatePredictor CreatePredictorBacktestExportJob DeleteDataset DeleteDatasetGroup DeleteDatasetImportJob DeleteForecast DeleteForecastExportJob DeletePredictor DeletePredictorBacktestExportJob DeleteResourceTree DescribeDataset DescribeDatasetGroup DescribeDatasetImportJob DescribeForecast DescribeForecastExportJob DescribePredictor DescribePredictorBacktestExportJob GetAccuracyMetrics ListDatasetGroups ListDatasetImportJobs ListDatasets ListForecastExportJobs ListForecasts ListPredictorBacktestExportJobs ListPredictors ListTagsForResource StopResource TagResource UntagResource UpdateDatasetGroup / } 1; ### main pod documentation begin ### =head1 NAME Paws::Forecast - Perl Interface to AWS Amazon Forecast Service =head1 SYNOPSIS use Paws; my $obj = Paws->service('Forecast'); my $res = $obj->Method( Arg1 => $val1, Arg2 => [ 'V1', 'V2' ], # if Arg3 is an object, the HashRef will be used as arguments to the constructor # of the arguments type Arg3 => { Att1 => 'Val1' }, # if Arg4 is an array of objects, the HashRefs will be passed as arguments to # the constructor of the arguments type Arg4 => [ { Att1 => 'Val1' }, { Att1 => 'Val2' } ], ); =head1 DESCRIPTION Provides APIs for creating and managing Amazon Forecast resources. For the AWS API documentation, see L =head1 METHODS =head2 CreateDataset =over =item DatasetName => Str =item DatasetType => Str =item Domain => Str =item Schema => L =item [DataFrequency => Str] =item [EncryptionConfig => L] =item [Tags => ArrayRef[L]] =back Each argument is described in detail in: L Returns: a L instance 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: =over =item * I > - How frequently your historical time-series data is collected. =item * I > and I< C > - 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. =item * I > - A schema specifies the fields in the dataset, including the field name and data type. =back After creating a dataset, you import your training data into it and add the dataset to a dataset group. You use the dataset group to create a predictor. For more information, see howitworks-datasets-groups. To get a list of all your datasets, use the ListDatasets operation. For example Forecast datasets, see the Amazon Forecast Sample GitHub repository (https://github.com/aws-samples/amazon-forecast-samples). The C of a dataset must be C before you can import training data. Use the DescribeDataset operation to get the status. =head2 CreateDatasetGroup =over =item DatasetGroupName => Str =item Domain => Str =item [DatasetArns => ArrayRef[Str|Undef]] =item [Tags => ArrayRef[L]] =back Each argument is described in detail in: L Returns: a L instance Creates a dataset group, which holds a collection of related datasets. You can add datasets to the dataset group when you create the dataset group, or later by using the UpdateDatasetGroup operation. After creating a dataset group and adding datasets, you use the dataset group when you create a predictor. For more information, see howitworks-datasets-groups. To get a list of all your datasets groups, use the ListDatasetGroups operation. The C of a dataset group must be C before you can use the dataset group to create a predictor. To get the status, use the DescribeDatasetGroup operation. =head2 CreateDatasetImportJob =over =item DatasetArn => Str =item DatasetImportJobName => Str =item DataSource => L =item [GeolocationFormat => Str] =item [Tags => ArrayRef[L]] =item [TimestampFormat => Str] =item [TimeZone => Str] =item [UseGeolocationForTimeZone => Bool] =back Each argument is described in detail in: L Returns: a L instance Imports your training data to an Amazon Forecast dataset. You provide the location of your training data in an Amazon Simple Storage Service (Amazon S3) bucket and the Amazon Resource Name (ARN) of the dataset that you want to import the data to. You must specify a DataSource object that includes an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data, as Amazon Forecast makes a copy of your data and processes it in an internal AWS system. For more information, see aws-forecast-iam-roles. The training data must be in CSV format. The delimiter must be a comma (,). You can specify the path to a specific CSV file, the S3 bucket, or to a folder in the S3 bucket. For the latter two cases, Amazon Forecast imports all files up to the limit of 10,000 files. Because dataset imports are not aggregated, your most recent dataset import is the one that is used when training a predictor or generating a forecast. Make sure that your most recent dataset import contains all of the data you want to model off of, and not just the new data collected since the previous import. To get a list of all your dataset import jobs, filtered by specified criteria, use the ListDatasetImportJobs operation. =head2 CreateForecast =over =item ForecastName => Str =item PredictorArn => Str =item [ForecastTypes => ArrayRef[Str|Undef]] =item [Tags => ArrayRef[L]] =back Each argument is described in detail in: L Returns: a L instance Creates a forecast for each item in the C 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 C 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 C of the forecast must be C before you can query or export the forecast. Use the DescribeForecast operation to get the status. =head2 CreateForecastExportJob =over =item Destination => L =item ForecastArn => Str =item ForecastExportJobName => Str =item [Tags => ArrayRef[L]] =back Each argument is described in detail in: L Returns: a L instance 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: EForecastExportJobNameE_EExportTimestampE_EPartNumberE where the EExportTimestampE component is in Java SimpleDateFormat (yyyy-MM-ddTHH-mm-ssZ). You must specify a DataDestination object that includes an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles. For more information, see howitworks-forecast. To get a list of all your forecast export jobs, use the ListForecastExportJobs operation. The C of the forecast export job must be C before you can access the forecast in your Amazon S3 bucket. To get the status, use the DescribeForecastExportJob operation. =head2 CreatePredictor =over =item FeaturizationConfig => L =item ForecastHorizon => Int =item InputDataConfig => L =item PredictorName => Str =item [AlgorithmArn => Str] =item [AutoMLOverrideStrategy => Str] =item [EncryptionConfig => L] =item [EvaluationParameters => L] =item [ForecastTypes => ArrayRef[Str|Undef]] =item [HPOConfig => L] =item [PerformAutoML => Bool] =item [PerformHPO => Bool] =item [Tags => ArrayRef[L]] =item [TrainingParameters => L] =back Each argument is described in detail in: L Returns: a L instance 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 C dataset to improve model training. For more information, see FeaturizationConfig. For RELATED_TIME_SERIES datasets, C verifies that the C specified when the dataset was created matches the C. 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 C. B If you want Amazon Forecast to evaluate each algorithm and choose the one that minimizes the C, set C to C. The C 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: =over =item * C =item * C =item * C =item * C =back To get a list of all of your predictors, use the ListPredictors operation. Before you can use the predictor to create a forecast, the C of the predictor must be C, signifying that training has completed. To get the status, use the DescribePredictor operation. =head2 CreatePredictorBacktestExportJob =over =item Destination => L =item PredictorArn => Str =item PredictorBacktestExportJobName => Str =item [Tags => ArrayRef[L]] =back Each argument is described in detail in: L Returns: a L instance Exports backtest forecasts and accuracy metrics generated by the CreatePredictor operation. Two folders containing CSV files are exported to your specified S3 bucket. The export file names will match the following conventions: CExportJobNameE_EExportTimestampE_EPartNumberE.csv> The EExportTimestampE component is in Java SimpleDate format (yyyy-MM-ddTHH-mm-ssZ). You must specify a DataDestination object that includes an Amazon S3 bucket and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles. The C of the export job must be C before you can access the export in your Amazon S3 bucket. To get the status, use the DescribePredictorBacktestExportJob operation. =head2 DeleteDataset =over =item DatasetArn => Str =back Each argument is described in detail in: L Returns: nothing Deletes an Amazon Forecast dataset that was created using the CreateDataset operation. You can only delete datasets that have a status of C or C. 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 operation, omitting the deleted dataset's ARN. =head2 DeleteDatasetGroup =over =item DatasetGroupArn => Str =back Each argument is described in detail in: L Returns: nothing Deletes a dataset group created using the CreateDatasetGroup operation. You can only delete dataset groups that have a status of C, C, or C. To get the status, use the DescribeDatasetGroup operation. This operation deletes only the dataset group, not the datasets in the group. =head2 DeleteDatasetImportJob =over =item DatasetImportJobArn => Str =back Each argument is described in detail in: L Returns: nothing Deletes a dataset import job created using the CreateDatasetImportJob operation. You can delete only dataset import jobs that have a status of C or C. To get the status, use the DescribeDatasetImportJob operation. =head2 DeleteForecast =over =item ForecastArn => Str =back Each argument is described in detail in: L Returns: nothing Deletes a forecast created using the CreateForecast operation. You can delete only forecasts that have a status of C or C. 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. =head2 DeleteForecastExportJob =over =item ForecastExportJobArn => Str =back Each argument is described in detail in: L Returns: nothing Deletes a forecast export job created using the CreateForecastExportJob operation. You can delete only export jobs that have a status of C or C. To get the status, use the DescribeForecastExportJob operation. =head2 DeletePredictor =over =item PredictorArn => Str =back Each argument is described in detail in: L Returns: nothing Deletes a predictor created using the CreatePredictor operation. You can delete only predictor that have a status of C or C. To get the status, use the DescribePredictor operation. =head2 DeletePredictorBacktestExportJob =over =item PredictorBacktestExportJobArn => Str =back Each argument is described in detail in: L Returns: nothing Deletes a predictor backtest export job. =head2 DeleteResourceTree =over =item ResourceArn => Str =back Each argument is described in detail in: L Returns: nothing 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: =over =item * B: dataset import jobs =item * B: predictors, predictor backtest export jobs, forecasts, forecast export jobs =item * B: predictor backtest export jobs, forecasts, forecast export jobs =item * B: forecast export jobs =back C will only delete Amazon Forecast resources, and will not delete datasets or exported files stored in Amazon S3. =head2 DescribeDataset =over =item DatasetArn => Str =back Each argument is described in detail in: L Returns: a L instance Describes an Amazon Forecast dataset created using the CreateDataset operation. In addition to listing the parameters specified in the C request, this operation includes the following dataset properties: =over =item * C =item * C =item * C =back =head2 DescribeDatasetGroup =over =item DatasetGroupArn => Str =back Each argument is described in detail in: L Returns: a L instance Describes a dataset group created using the CreateDatasetGroup operation. In addition to listing the parameters provided in the C request, this operation includes the following properties: =over =item * C - The datasets belonging to the group. =item * C =item * C =item * C =back =head2 DescribeDatasetImportJob =over =item DatasetImportJobArn => Str =back Each argument is described in detail in: L Returns: a L instance Describes a dataset import job created using the CreateDatasetImportJob operation. In addition to listing the parameters provided in the C request, this operation includes the following properties: =over =item * C =item * C =item * C =item * C =item * C =item * C - If an error occurred, information about the error. =back =head2 DescribeForecast =over =item ForecastArn => Str =back Each argument is described in detail in: L Returns: a L instance Describes a forecast created using the CreateForecast operation. In addition to listing the properties provided in the C request, this operation lists the following properties: =over =item * C - The dataset group that provided the training data. =item * C =item * C =item * C =item * C - If an error occurred, information about the error. =back =head2 DescribeForecastExportJob =over =item ForecastExportJobArn => Str =back Each argument is described in detail in: L Returns: a L instance Describes a forecast export job created using the CreateForecastExportJob operation. In addition to listing the properties provided by the user in the C request, this operation lists the following properties: =over =item * C =item * C =item * C =item * C - If an error occurred, information about the error. =back =head2 DescribePredictor =over =item PredictorArn => Str =back Each argument is described in detail in: L Returns: a L instance Describes a predictor created using the CreatePredictor operation. In addition to listing the properties provided in the C request, this operation lists the following properties: =over =item * C - The dataset import jobs used to import training data. =item * C - If AutoML is performed, the algorithms that were evaluated. =item * C =item * C =item * C =item * C - If an error occurred, information about the error. =back =head2 DescribePredictorBacktestExportJob =over =item PredictorBacktestExportJobArn => Str =back Each argument is described in detail in: L Returns: a L instance Describes a predictor backtest export job created using the CreatePredictorBacktestExportJob operation. In addition to listing the properties provided by the user in the C request, this operation lists the following properties: =over =item * C =item * C =item * C =item * C (if an error occurred) =back =head2 GetAccuracyMetrics =over =item PredictorArn => Str =back Each argument is described in detail in: L Returns: a L instance 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 (https://docs.aws.amazon.com/forecast/latest/dg/metrics.html). This operation generates metrics for each backtest window that was evaluated. The number of backtest windows (C) is specified using the EvaluationParameters object, which is optionally included in the C request. If C isn't specified, the number defaults to one. The parameters of the C method determine which items contribute to the metrics. If you want all items to contribute, specify C. If you want only those items that have complete data in the range being evaluated to contribute, specify C. For more information, see FeaturizationMethod. Before you can get accuracy metrics, the C of the predictor must be C, signifying that training has completed. To get the status, use the DescribePredictor operation. =head2 ListDatasetGroups =over =item [MaxResults => Int] =item [NextToken => Str] =back Each argument is described in detail in: L Returns: a L instance 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. =head2 ListDatasetImportJobs =over =item [Filters => ArrayRef[L]] =item [MaxResults => Int] =item [NextToken => Str] =back Each argument is described in detail in: L Returns: a L instance 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. =head2 ListDatasets =over =item [MaxResults => Int] =item [NextToken => Str] =back Each argument is described in detail in: L Returns: a L instance 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. =head2 ListForecastExportJobs =over =item [Filters => ArrayRef[L]] =item [MaxResults => Int] =item [NextToken => Str] =back Each argument is described in detail in: L Returns: a L instance 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. =head2 ListForecasts =over =item [Filters => ArrayRef[L]] =item [MaxResults => Int] =item [NextToken => Str] =back Each argument is described in detail in: L Returns: a L instance 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. =head2 ListPredictorBacktestExportJobs =over =item [Filters => ArrayRef[L]] =item [MaxResults => Int] =item [NextToken => Str] =back Each argument is described in detail in: L Returns: a L instance 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. =head2 ListPredictors =over =item [Filters => ArrayRef[L]] =item [MaxResults => Int] =item [NextToken => Str] =back Each argument is described in detail in: L Returns: a L instance Returns a list of predictors created using the CreatePredictor operation. 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 DescribePredictor operation. You can filter the list using an array of Filter objects. =head2 ListTagsForResource =over =item ResourceArn => Str =back Each argument is described in detail in: L Returns: a L instance Lists the tags for an Amazon Forecast resource. =head2 StopResource =over =item ResourceArn => Str =back Each argument is described in detail in: L Returns: nothing Stops a resource. The resource undergoes the following states: C and C. You cannot resume a resource once it has been stopped. This operation can be applied to the following resources (and their corresponding child resources): =over =item * Dataset Import Job =item * Predictor Job =item * Forecast Job =item * Forecast Export Job =item * Predictor Backtest Export Job =back =head2 TagResource =over =item ResourceArn => Str =item Tags => ArrayRef[L] =back Each argument is described in detail in: L Returns: a L instance Associates the specified tags to a resource with the specified C. 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. =head2 UntagResource =over =item ResourceArn => Str =item TagKeys => ArrayRef[Str|Undef] =back Each argument is described in detail in: L Returns: a L instance Deletes the specified tags from a resource. =head2 UpdateDatasetGroup =over =item DatasetArns => ArrayRef[Str|Undef] =item DatasetGroupArn => Str =back Each argument is described in detail in: L Returns: a L instance Replaces the datasets in a dataset group with the specified datasets. The C of the dataset group must be C before you can use the dataset group to create a predictor. Use the DescribeDatasetGroup operation to get the status. =head1 PAGINATORS Paginator methods are helpers that repetively call methods that return partial results =head2 ListAllDatasetGroups(sub { },[MaxResults => Int, NextToken => Str]) =head2 ListAllDatasetGroups([MaxResults => Int, NextToken => Str]) If passed a sub as first parameter, it will call the sub for each element found in : - DatasetGroups, passing the object as the first parameter, and the string 'DatasetGroups' as the second parameter If not, it will return a a L instance with all the Cs; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory. =head2 ListAllDatasetImportJobs(sub { },[Filters => ArrayRef[L], MaxResults => Int, NextToken => Str]) =head2 ListAllDatasetImportJobs([Filters => ArrayRef[L], MaxResults => Int, NextToken => Str]) If passed a sub as first parameter, it will call the sub for each element found in : - DatasetImportJobs, passing the object as the first parameter, and the string 'DatasetImportJobs' as the second parameter If not, it will return a a L instance with all the Cs; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory. =head2 ListAllDatasets(sub { },[MaxResults => Int, NextToken => Str]) =head2 ListAllDatasets([MaxResults => Int, NextToken => Str]) If passed a sub as first parameter, it will call the sub for each element found in : - Datasets, passing the object as the first parameter, and the string 'Datasets' as the second parameter If not, it will return a a L instance with all the Cs; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory. =head2 ListAllForecastExportJobs(sub { },[Filters => ArrayRef[L], MaxResults => Int, NextToken => Str]) =head2 ListAllForecastExportJobs([Filters => ArrayRef[L], MaxResults => Int, NextToken => Str]) If passed a sub as first parameter, it will call the sub for each element found in : - ForecastExportJobs, passing the object as the first parameter, and the string 'ForecastExportJobs' as the second parameter If not, it will return a a L instance with all the Cs; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory. =head2 ListAllForecasts(sub { },[Filters => ArrayRef[L], MaxResults => Int, NextToken => Str]) =head2 ListAllForecasts([Filters => ArrayRef[L], MaxResults => Int, NextToken => Str]) If passed a sub as first parameter, it will call the sub for each element found in : - Forecasts, passing the object as the first parameter, and the string 'Forecasts' as the second parameter If not, it will return a a L instance with all the Cs; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory. =head2 ListAllPredictorBacktestExportJobs(sub { },[Filters => ArrayRef[L], MaxResults => Int, NextToken => Str]) =head2 ListAllPredictorBacktestExportJobs([Filters => ArrayRef[L], MaxResults => Int, NextToken => Str]) If passed a sub as first parameter, it will call the sub for each element found in : - PredictorBacktestExportJobs, passing the object as the first parameter, and the string 'PredictorBacktestExportJobs' as the second parameter If not, it will return a a L instance with all the Cs; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory. =head2 ListAllPredictors(sub { },[Filters => ArrayRef[L], MaxResults => Int, NextToken => Str]) =head2 ListAllPredictors([Filters => ArrayRef[L], MaxResults => Int, NextToken => Str]) If passed a sub as first parameter, it will call the sub for each element found in : - Predictors, passing the object as the first parameter, and the string 'Predictors' as the second parameter If not, it will return a a L instance with all the Cs; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory. =head1 SEE ALSO This service class forms part of L =head1 BUGS and CONTRIBUTIONS The source code is located here: L Please report bugs to: L =cut