%% WARNING: DO NOT EDIT, AUTO-GENERATED CODE! %% See https://github.com/aws-beam/aws-codegen for more details. %% @doc Provides APIs for creating and managing Amazon Forecast resources. -module(aws_forecast). -export([create_auto_predictor/2, create_auto_predictor/3, create_dataset/2, create_dataset/3, create_dataset_group/2, create_dataset_group/3, create_dataset_import_job/2, create_dataset_import_job/3, create_explainability/2, create_explainability/3, create_explainability_export/2, create_explainability_export/3, create_forecast/2, create_forecast/3, create_forecast_export_job/2, create_forecast_export_job/3, create_monitor/2, create_monitor/3, create_predictor/2, create_predictor/3, create_predictor_backtest_export_job/2, create_predictor_backtest_export_job/3, create_what_if_analysis/2, create_what_if_analysis/3, create_what_if_forecast/2, create_what_if_forecast/3, create_what_if_forecast_export/2, create_what_if_forecast_export/3, delete_dataset/2, delete_dataset/3, delete_dataset_group/2, delete_dataset_group/3, delete_dataset_import_job/2, delete_dataset_import_job/3, delete_explainability/2, delete_explainability/3, delete_explainability_export/2, delete_explainability_export/3, delete_forecast/2, delete_forecast/3, delete_forecast_export_job/2, delete_forecast_export_job/3, delete_monitor/2, delete_monitor/3, delete_predictor/2, delete_predictor/3, delete_predictor_backtest_export_job/2, delete_predictor_backtest_export_job/3, delete_resource_tree/2, delete_resource_tree/3, delete_what_if_analysis/2, delete_what_if_analysis/3, delete_what_if_forecast/2, delete_what_if_forecast/3, delete_what_if_forecast_export/2, delete_what_if_forecast_export/3, describe_auto_predictor/2, describe_auto_predictor/3, describe_dataset/2, describe_dataset/3, describe_dataset_group/2, describe_dataset_group/3, describe_dataset_import_job/2, describe_dataset_import_job/3, describe_explainability/2, describe_explainability/3, describe_explainability_export/2, describe_explainability_export/3, describe_forecast/2, describe_forecast/3, describe_forecast_export_job/2, describe_forecast_export_job/3, describe_monitor/2, describe_monitor/3, describe_predictor/2, describe_predictor/3, describe_predictor_backtest_export_job/2, describe_predictor_backtest_export_job/3, describe_what_if_analysis/2, describe_what_if_analysis/3, describe_what_if_forecast/2, describe_what_if_forecast/3, describe_what_if_forecast_export/2, describe_what_if_forecast_export/3, get_accuracy_metrics/2, get_accuracy_metrics/3, list_dataset_groups/2, list_dataset_groups/3, list_dataset_import_jobs/2, list_dataset_import_jobs/3, list_datasets/2, list_datasets/3, list_explainabilities/2, list_explainabilities/3, list_explainability_exports/2, list_explainability_exports/3, list_forecast_export_jobs/2, list_forecast_export_jobs/3, list_forecasts/2, list_forecasts/3, list_monitor_evaluations/2, list_monitor_evaluations/3, list_monitors/2, list_monitors/3, list_predictor_backtest_export_jobs/2, list_predictor_backtest_export_jobs/3, list_predictors/2, list_predictors/3, list_tags_for_resource/2, list_tags_for_resource/3, list_what_if_analyses/2, list_what_if_analyses/3, list_what_if_forecast_exports/2, list_what_if_forecast_exports/3, list_what_if_forecasts/2, list_what_if_forecasts/3, resume_resource/2, resume_resource/3, stop_resource/2, stop_resource/3, tag_resource/2, tag_resource/3, untag_resource/2, untag_resource/3, update_dataset_group/2, update_dataset_group/3]). -include_lib("hackney/include/hackney_lib.hrl"). %% Example: %% list_forecast_export_jobs_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_forecast_export_jobs_request() :: #{binary() => any()}. %% Example: %% weighted_quantile_loss() :: #{ %% <<"LossValue">> => float(), %% <<"Quantile">> => float() %% } -type weighted_quantile_loss() :: #{binary() => any()}. %% Example: %% list_what_if_forecasts_response() :: #{ %% <<"NextToken">> => string(), %% <<"WhatIfForecasts">> => list(what_if_forecast_summary()) %% } -type list_what_if_forecasts_response() :: #{binary() => any()}. %% Example: %% describe_explainability_export_request() :: #{ %% <<"ExplainabilityExportArn">> := string() %% } -type describe_explainability_export_request() :: #{binary() => any()}. %% Example: %% tag_resource_request() :: #{ %% <<"ResourceArn">> := string(), %% <<"Tags">> := list(tag()) %% } -type tag_resource_request() :: #{binary() => any()}. %% Example: %% create_predictor_request() :: #{ %% <<"AlgorithmArn">> => string(), %% <<"AutoMLOverrideStrategy">> => list(any()), %% <<"EncryptionConfig">> => encryption_config(), %% <<"EvaluationParameters">> => evaluation_parameters(), %% <<"FeaturizationConfig">> := featurization_config(), %% <<"ForecastHorizon">> := integer(), %% <<"ForecastTypes">> => list(string()), %% <<"HPOConfig">> => hyper_parameter_tuning_job_config(), %% <<"InputDataConfig">> := input_data_config(), %% <<"OptimizationMetric">> => list(any()), %% <<"PerformAutoML">> => boolean(), %% <<"PerformHPO">> => boolean(), %% <<"PredictorName">> := string(), %% <<"Tags">> => list(tag()), %% <<"TrainingParameters">> => map() %% } -type create_predictor_request() :: #{binary() => any()}. %% Example: %% integer_parameter_range() :: #{ %% <<"MaxValue">> => integer(), %% <<"MinValue">> => integer(), %% <<"Name">> => string(), %% <<"ScalingType">> => list(any()) %% } -type integer_parameter_range() :: #{binary() => any()}. %% Example: %% describe_explainability_export_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"Destination">> => data_destination(), %% <<"ExplainabilityArn">> => string(), %% <<"ExplainabilityExportArn">> => string(), %% <<"ExplainabilityExportName">> => string(), %% <<"Format">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string() %% } -type describe_explainability_export_response() :: #{binary() => any()}. %% Example: %% encryption_config() :: #{ %% <<"KMSKeyArn">> => string(), %% <<"RoleArn">> => string() %% } -type encryption_config() :: #{binary() => any()}. %% Example: %% reference_predictor_summary() :: #{ %% <<"Arn">> => string(), %% <<"State">> => list(any()) %% } -type reference_predictor_summary() :: #{binary() => any()}. %% Example: %% delete_monitor_request() :: #{ %% <<"MonitorArn">> := string() %% } -type delete_monitor_request() :: #{binary() => any()}. %% Example: %% list_monitors_response() :: #{ %% <<"Monitors">> => list(monitor_summary()), %% <<"NextToken">> => string() %% } -type list_monitors_response() :: #{binary() => any()}. %% Example: %% list_monitor_evaluations_response() :: #{ %% <<"NextToken">> => string(), %% <<"PredictorMonitorEvaluations">> => list(predictor_monitor_evaluation()) %% } -type list_monitor_evaluations_response() :: #{binary() => any()}. %% Example: %% delete_forecast_request() :: #{ %% <<"ForecastArn">> := string() %% } -type delete_forecast_request() :: #{binary() => any()}. %% Example: %% describe_forecast_request() :: #{ %% <<"ForecastArn">> := string() %% } -type describe_forecast_request() :: #{binary() => any()}. %% Example: %% get_accuracy_metrics_response() :: #{ %% <<"AutoMLOverrideStrategy">> => list(any()), %% <<"IsAutoPredictor">> => boolean(), %% <<"OptimizationMetric">> => list(any()), %% <<"PredictorEvaluationResults">> => list(evaluation_result()) %% } -type get_accuracy_metrics_response() :: #{binary() => any()}. %% Example: %% predictor_monitor_evaluation() :: #{ %% <<"EvaluationState">> => string(), %% <<"EvaluationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"MetricResults">> => list(metric_result()), %% <<"MonitorArn">> => string(), %% <<"MonitorDataSource">> => monitor_data_source(), %% <<"NumItemsEvaluated">> => float(), %% <<"PredictorEvent">> => predictor_event(), %% <<"ResourceArn">> => string(), %% <<"WindowEndDatetime">> => non_neg_integer(), %% <<"WindowStartDatetime">> => non_neg_integer() %% } -type predictor_monitor_evaluation() :: #{binary() => any()}. %% Example: %% input_data_config() :: #{ %% <<"DatasetGroupArn">> => string(), %% <<"SupplementaryFeatures">> => list(supplementary_feature()) %% } -type input_data_config() :: #{binary() => any()}. %% Example: %% untag_resource_response() :: #{ %% } -type untag_resource_response() :: #{binary() => any()}. %% Example: %% resource_in_use_exception() :: #{ %% <<"Message">> => string() %% } -type resource_in_use_exception() :: #{binary() => any()}. %% Example: %% describe_auto_predictor_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"DataConfig">> => data_config(), %% <<"DatasetImportJobArns">> => list(string()), %% <<"EncryptionConfig">> => encryption_config(), %% <<"EstimatedTimeRemainingInMinutes">> => float(), %% <<"ExplainabilityInfo">> => explainability_info(), %% <<"ForecastDimensions">> => list(string()), %% <<"ForecastFrequency">> => string(), %% <<"ForecastHorizon">> => integer(), %% <<"ForecastTypes">> => list(string()), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"MonitorInfo">> => monitor_info(), %% <<"OptimizationMetric">> => list(any()), %% <<"PredictorArn">> => string(), %% <<"PredictorName">> => string(), %% <<"ReferencePredictorSummary">> => reference_predictor_summary(), %% <<"Status">> => string(), %% <<"TimeAlignmentBoundary">> => time_alignment_boundary() %% } -type describe_auto_predictor_response() :: #{binary() => any()}. %% Example: %% monitor_config() :: #{ %% <<"MonitorName">> => string() %% } -type monitor_config() :: #{binary() => any()}. %% Example: %% describe_dataset_import_job_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"DataSize">> => float(), %% <<"DataSource">> => data_source(), %% <<"DatasetArn">> => string(), %% <<"DatasetImportJobArn">> => string(), %% <<"DatasetImportJobName">> => string(), %% <<"EstimatedTimeRemainingInMinutes">> => float(), %% <<"FieldStatistics">> => map(), %% <<"Format">> => string(), %% <<"GeolocationFormat">> => string(), %% <<"ImportMode">> => list(any()), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string(), %% <<"TimeZone">> => string(), %% <<"TimestampFormat">> => string(), %% <<"UseGeolocationForTimeZone">> => boolean() %% } -type describe_dataset_import_job_response() :: #{binary() => any()}. %% Example: %% attribute_config() :: #{ %% <<"AttributeName">> => string(), %% <<"Transformations">> => map() %% } -type attribute_config() :: #{binary() => any()}. %% Example: %% list_what_if_analyses_response() :: #{ %% <<"NextToken">> => string(), %% <<"WhatIfAnalyses">> => list(what_if_analysis_summary()) %% } -type list_what_if_analyses_response() :: #{binary() => any()}. %% Example: %% featurization_method() :: #{ %% <<"FeaturizationMethodName">> => list(any()), %% <<"FeaturizationMethodParameters">> => map() %% } -type featurization_method() :: #{binary() => any()}. %% Example: %% parameter_ranges() :: #{ %% <<"CategoricalParameterRanges">> => list(categorical_parameter_range()), %% <<"ContinuousParameterRanges">> => list(continuous_parameter_range()), %% <<"IntegerParameterRanges">> => list(integer_parameter_range()) %% } -type parameter_ranges() :: #{binary() => any()}. %% Example: %% create_predictor_response() :: #{ %% <<"PredictorArn">> => string() %% } -type create_predictor_response() :: #{binary() => any()}. %% Example: %% list_forecast_export_jobs_response() :: #{ %% <<"ForecastExportJobs">> => list(forecast_export_job_summary()), %% <<"NextToken">> => string() %% } -type list_forecast_export_jobs_response() :: #{binary() => any()}. %% Example: %% create_explainability_export_request() :: #{ %% <<"Destination">> := data_destination(), %% <<"ExplainabilityArn">> := string(), %% <<"ExplainabilityExportName">> := string(), %% <<"Format">> => string(), %% <<"Tags">> => list(tag()) %% } -type create_explainability_export_request() :: #{binary() => any()}. %% Example: %% update_dataset_group_response() :: #{ %% } -type update_dataset_group_response() :: #{binary() => any()}. %% Example: %% list_datasets_request() :: #{ %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_datasets_request() :: #{binary() => any()}. %% Example: %% list_what_if_forecast_exports_response() :: #{ %% <<"NextToken">> => string(), %% <<"WhatIfForecastExports">> => list(what_if_forecast_export_summary()) %% } -type list_what_if_forecast_exports_response() :: #{binary() => any()}. %% Example: %% evaluation_parameters() :: #{ %% <<"BackTestWindowOffset">> => integer(), %% <<"NumberOfBacktestWindows">> => integer() %% } -type evaluation_parameters() :: #{binary() => any()}. %% Example: %% featurization_config() :: #{ %% <<"Featurizations">> => list(featurization()), %% <<"ForecastDimensions">> => list(string()), %% <<"ForecastFrequency">> => string() %% } -type featurization_config() :: #{binary() => any()}. %% Example: %% list_monitors_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_monitors_request() :: #{binary() => any()}. %% Example: %% list_predictors_response() :: #{ %% <<"NextToken">> => string(), %% <<"Predictors">> => list(predictor_summary()) %% } -type list_predictors_response() :: #{binary() => any()}. %% Example: %% create_predictor_backtest_export_job_response() :: #{ %% <<"PredictorBacktestExportJobArn">> => string() %% } -type create_predictor_backtest_export_job_response() :: #{binary() => any()}. %% Example: %% untag_resource_request() :: #{ %% <<"ResourceArn">> := string(), %% <<"TagKeys">> := list(string()) %% } -type untag_resource_request() :: #{binary() => any()}. %% Example: %% monitor_info() :: #{ %% <<"MonitorArn">> => string(), %% <<"Status">> => string() %% } -type monitor_info() :: #{binary() => any()}. %% Example: %% create_monitor_response() :: #{ %% <<"MonitorArn">> => string() %% } -type create_monitor_response() :: #{binary() => any()}. %% Example: %% list_what_if_forecasts_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_what_if_forecasts_request() :: #{binary() => any()}. %% Example: %% explainability_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"ExplainabilityArn">> => string(), %% <<"ExplainabilityConfig">> => explainability_config(), %% <<"ExplainabilityName">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"ResourceArn">> => string(), %% <<"Status">> => string() %% } -type explainability_summary() :: #{binary() => any()}. %% Example: %% create_dataset_group_request() :: #{ %% <<"DatasetArns">> => list(string()), %% <<"DatasetGroupName">> := string(), %% <<"Domain">> := list(any()), %% <<"Tags">> => list(tag()) %% } -type create_dataset_group_request() :: #{binary() => any()}. %% Example: %% describe_forecast_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"DatasetGroupArn">> => string(), %% <<"EstimatedTimeRemainingInMinutes">> => float(), %% <<"ForecastArn">> => string(), %% <<"ForecastName">> => string(), %% <<"ForecastTypes">> => list(string()), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"PredictorArn">> => string(), %% <<"Status">> => string(), %% <<"TimeSeriesSelector">> => time_series_selector() %% } -type describe_forecast_response() :: #{binary() => any()}. %% Example: %% describe_what_if_forecast_export_request() :: #{ %% <<"WhatIfForecastExportArn">> := string() %% } -type describe_what_if_forecast_export_request() :: #{binary() => any()}. %% Example: %% baseline() :: #{ %% <<"PredictorBaseline">> => predictor_baseline() %% } -type baseline() :: #{binary() => any()}. %% Example: %% time_series_selector() :: #{ %% <<"TimeSeriesIdentifiers">> => time_series_identifiers() %% } -type time_series_selector() :: #{binary() => any()}. %% Example: %% predictor_execution_details() :: #{ %% <<"PredictorExecutions">> => list(predictor_execution()) %% } -type predictor_execution_details() :: #{binary() => any()}. %% Example: %% forecast_summary() :: #{ %% <<"CreatedUsingAutoPredictor">> => boolean(), %% <<"CreationTime">> => non_neg_integer(), %% <<"DatasetGroupArn">> => string(), %% <<"ForecastArn">> => string(), %% <<"ForecastName">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"PredictorArn">> => string(), %% <<"Status">> => string() %% } -type forecast_summary() :: #{binary() => any()}. %% Example: %% describe_predictor_response() :: #{ %% <<"AlgorithmArn">> => string(), %% <<"AutoMLAlgorithmArns">> => list(string()), %% <<"AutoMLOverrideStrategy">> => list(any()), %% <<"CreationTime">> => non_neg_integer(), %% <<"DatasetImportJobArns">> => list(string()), %% <<"EncryptionConfig">> => encryption_config(), %% <<"EstimatedTimeRemainingInMinutes">> => float(), %% <<"EvaluationParameters">> => evaluation_parameters(), %% <<"FeaturizationConfig">> => featurization_config(), %% <<"ForecastHorizon">> => integer(), %% <<"ForecastTypes">> => list(string()), %% <<"HPOConfig">> => hyper_parameter_tuning_job_config(), %% <<"InputDataConfig">> => input_data_config(), %% <<"IsAutoPredictor">> => boolean(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"OptimizationMetric">> => list(any()), %% <<"PerformAutoML">> => boolean(), %% <<"PerformHPO">> => boolean(), %% <<"PredictorArn">> => string(), %% <<"PredictorExecutionDetails">> => predictor_execution_details(), %% <<"PredictorName">> => string(), %% <<"Status">> => string(), %% <<"TrainingParameters">> => map() %% } -type describe_predictor_response() :: #{binary() => any()}. %% Example: %% what_if_forecast_export_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"Destination">> => data_destination(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string(), %% <<"WhatIfForecastArns">> => list(string()), %% <<"WhatIfForecastExportArn">> => string(), %% <<"WhatIfForecastExportName">> => string() %% } -type what_if_forecast_export_summary() :: #{binary() => any()}. %% Example: %% test_window_summary() :: #{ %% <<"Message">> => string(), %% <<"Status">> => string(), %% <<"TestWindowEnd">> => non_neg_integer(), %% <<"TestWindowStart">> => non_neg_integer() %% } -type test_window_summary() :: #{binary() => any()}. %% Example: %% stop_resource_request() :: #{ %% <<"ResourceArn">> := string() %% } -type stop_resource_request() :: #{binary() => any()}. %% Example: %% list_explainability_exports_response() :: #{ %% <<"ExplainabilityExports">> => list(explainability_export_summary()), %% <<"NextToken">> => string() %% } -type list_explainability_exports_response() :: #{binary() => any()}. %% Example: %% create_auto_predictor_response() :: #{ %% <<"PredictorArn">> => string() %% } -type create_auto_predictor_response() :: #{binary() => any()}. %% Example: %% create_dataset_response() :: #{ %% <<"DatasetArn">> => string() %% } -type create_dataset_response() :: #{binary() => any()}. %% Example: %% metrics() :: #{ %% <<"AverageWeightedQuantileLoss">> => float(), %% <<"ErrorMetrics">> => list(error_metric()), %% <<"RMSE">> => float(), %% <<"WeightedQuantileLosses">> => list(weighted_quantile_loss()) %% } -type metrics() :: #{binary() => any()}. %% Example: %% explainability_export_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"Destination">> => data_destination(), %% <<"ExplainabilityExportArn">> => string(), %% <<"ExplainabilityExportName">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string() %% } -type explainability_export_summary() :: #{binary() => any()}. %% Example: %% describe_what_if_forecast_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"EstimatedTimeRemainingInMinutes">> => float(), %% <<"ForecastTypes">> => list(string()), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string(), %% <<"TimeSeriesReplacementsDataSource">> => time_series_replacements_data_source(), %% <<"TimeSeriesTransformations">> => list(time_series_transformation()), %% <<"WhatIfAnalysisArn">> => string(), %% <<"WhatIfForecastArn">> => string(), %% <<"WhatIfForecastName">> => string() %% } -type describe_what_if_forecast_response() :: #{binary() => any()}. %% Example: %% supplementary_feature() :: #{ %% <<"Name">> => string(), %% <<"Value">> => string() %% } -type supplementary_feature() :: #{binary() => any()}. %% Example: %% resource_not_found_exception() :: #{ %% <<"Message">> => string() %% } -type resource_not_found_exception() :: #{binary() => any()}. %% Example: %% list_dataset_import_jobs_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_dataset_import_jobs_request() :: #{binary() => any()}. %% Example: %% create_what_if_forecast_response() :: #{ %% <<"WhatIfForecastArn">> => string() %% } -type create_what_if_forecast_response() :: #{binary() => any()}. %% Example: %% forecast_export_job_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"Destination">> => data_destination(), %% <<"ForecastExportJobArn">> => string(), %% <<"ForecastExportJobName">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string() %% } -type forecast_export_job_summary() :: #{binary() => any()}. %% Example: %% describe_explainability_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"DataSource">> => data_source(), %% <<"EnableVisualization">> => boolean(), %% <<"EndDateTime">> => string(), %% <<"EstimatedTimeRemainingInMinutes">> => float(), %% <<"ExplainabilityArn">> => string(), %% <<"ExplainabilityConfig">> => explainability_config(), %% <<"ExplainabilityName">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"ResourceArn">> => string(), %% <<"Schema">> => schema(), %% <<"StartDateTime">> => string(), %% <<"Status">> => string() %% } -type describe_explainability_response() :: #{binary() => any()}. %% Example: %% explainability_info() :: #{ %% <<"ExplainabilityArn">> => string(), %% <<"Status">> => string() %% } -type explainability_info() :: #{binary() => any()}. %% Example: %% get_accuracy_metrics_request() :: #{ %% <<"PredictorArn">> := string() %% } -type get_accuracy_metrics_request() :: #{binary() => any()}. %% Example: %% dataset_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"DatasetArn">> => string(), %% <<"DatasetName">> => string(), %% <<"DatasetType">> => list(any()), %% <<"Domain">> => list(any()), %% <<"LastModificationTime">> => non_neg_integer() %% } -type dataset_summary() :: #{binary() => any()}. %% Example: %% tag() :: #{ %% <<"Key">> => string(), %% <<"Value">> => string() %% } -type tag() :: #{binary() => any()}. %% Example: %% describe_auto_predictor_request() :: #{ %% <<"PredictorArn">> := string() %% } -type describe_auto_predictor_request() :: #{binary() => any()}. %% Example: %% resume_resource_request() :: #{ %% <<"ResourceArn">> := string() %% } -type resume_resource_request() :: #{binary() => any()}. %% Example: %% s3_config() :: #{ %% <<"KMSKeyArn">> => string(), %% <<"Path">> => string(), %% <<"RoleArn">> => string() %% } -type s3_config() :: #{binary() => any()}. %% Example: %% delete_dataset_import_job_request() :: #{ %% <<"DatasetImportJobArn">> := string() %% } -type delete_dataset_import_job_request() :: #{binary() => any()}. %% Example: %% delete_forecast_export_job_request() :: #{ %% <<"ForecastExportJobArn">> := string() %% } -type delete_forecast_export_job_request() :: #{binary() => any()}. %% Example: %% time_series_transformation() :: #{ %% <<"Action">> => action(), %% <<"TimeSeriesConditions">> => list(time_series_condition()) %% } -type time_series_transformation() :: #{binary() => any()}. %% Example: %% data_config() :: #{ %% <<"AdditionalDatasets">> => list(additional_dataset()), %% <<"AttributeConfigs">> => list(attribute_config()), %% <<"DatasetGroupArn">> => string() %% } -type data_config() :: #{binary() => any()}. %% Example: %% invalid_next_token_exception() :: #{ %% <<"Message">> => string() %% } -type invalid_next_token_exception() :: #{binary() => any()}. %% Example: %% create_forecast_export_job_response() :: #{ %% <<"ForecastExportJobArn">> => string() %% } -type create_forecast_export_job_response() :: #{binary() => any()}. %% Example: %% create_what_if_forecast_request() :: #{ %% <<"Tags">> => list(tag()), %% <<"TimeSeriesReplacementsDataSource">> => time_series_replacements_data_source(), %% <<"TimeSeriesTransformations">> => list(time_series_transformation()), %% <<"WhatIfAnalysisArn">> := string(), %% <<"WhatIfForecastName">> := string() %% } -type create_what_if_forecast_request() :: #{binary() => any()}. %% Example: %% create_auto_predictor_request() :: #{ %% <<"DataConfig">> => data_config(), %% <<"EncryptionConfig">> => encryption_config(), %% <<"ExplainPredictor">> => boolean(), %% <<"ForecastDimensions">> => list(string()), %% <<"ForecastFrequency">> => string(), %% <<"ForecastHorizon">> => integer(), %% <<"ForecastTypes">> => list(string()), %% <<"MonitorConfig">> => monitor_config(), %% <<"OptimizationMetric">> => list(any()), %% <<"PredictorName">> := string(), %% <<"ReferencePredictorArn">> => string(), %% <<"Tags">> => list(tag()), %% <<"TimeAlignmentBoundary">> => time_alignment_boundary() %% } -type create_auto_predictor_request() :: #{binary() => any()}. %% Example: %% list_dataset_groups_response() :: #{ %% <<"DatasetGroups">> => list(dataset_group_summary()), %% <<"NextToken">> => string() %% } -type list_dataset_groups_response() :: #{binary() => any()}. %% Example: %% create_what_if_analysis_response() :: #{ %% <<"WhatIfAnalysisArn">> => string() %% } -type create_what_if_analysis_response() :: #{binary() => any()}. %% Example: %% list_what_if_analyses_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_what_if_analyses_request() :: #{binary() => any()}. %% Example: %% list_predictors_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_predictors_request() :: #{binary() => any()}. %% Example: %% describe_what_if_analysis_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"EstimatedTimeRemainingInMinutes">> => float(), %% <<"ForecastArn">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string(), %% <<"TimeSeriesSelector">> => time_series_selector(), %% <<"WhatIfAnalysisArn">> => string(), %% <<"WhatIfAnalysisName">> => string() %% } -type describe_what_if_analysis_response() :: #{binary() => any()}. %% Example: %% schema() :: #{ %% <<"Attributes">> => list(schema_attribute()) %% } -type schema() :: #{binary() => any()}. %% Example: %% list_dataset_groups_request() :: #{ %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_dataset_groups_request() :: #{binary() => any()}. %% Example: %% list_forecasts_response() :: #{ %% <<"Forecasts">> => list(forecast_summary()), %% <<"NextToken">> => string() %% } -type list_forecasts_response() :: #{binary() => any()}. %% Example: %% invalid_input_exception() :: #{ %% <<"Message">> => string() %% } -type invalid_input_exception() :: #{binary() => any()}. %% Example: %% delete_explainability_request() :: #{ %% <<"ExplainabilityArn">> := string() %% } -type delete_explainability_request() :: #{binary() => any()}. %% Example: %% list_tags_for_resource_response() :: #{ %% <<"Tags">> => list(tag()) %% } -type list_tags_for_resource_response() :: #{binary() => any()}. %% Example: %% list_predictor_backtest_export_jobs_response() :: #{ %% <<"NextToken">> => string(), %% <<"PredictorBacktestExportJobs">> => list(predictor_backtest_export_job_summary()) %% } -type list_predictor_backtest_export_jobs_response() :: #{binary() => any()}. %% Example: %% create_what_if_analysis_request() :: #{ %% <<"ForecastArn">> := string(), %% <<"Tags">> => list(tag()), %% <<"TimeSeriesSelector">> => time_series_selector(), %% <<"WhatIfAnalysisName">> := string() %% } -type create_what_if_analysis_request() :: #{binary() => any()}. %% Example: %% describe_what_if_analysis_request() :: #{ %% <<"WhatIfAnalysisArn">> := string() %% } -type describe_what_if_analysis_request() :: #{binary() => any()}. %% Example: %% baseline_metric() :: #{ %% <<"Name">> => string(), %% <<"Value">> => float() %% } -type baseline_metric() :: #{binary() => any()}. %% Example: %% categorical_parameter_range() :: #{ %% <<"Name">> => string(), %% <<"Values">> => list(string()) %% } -type categorical_parameter_range() :: #{binary() => any()}. %% Example: %% describe_predictor_backtest_export_job_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"Destination">> => data_destination(), %% <<"Format">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"PredictorArn">> => string(), %% <<"PredictorBacktestExportJobArn">> => string(), %% <<"PredictorBacktestExportJobName">> => string(), %% <<"Status">> => string() %% } -type describe_predictor_backtest_export_job_response() :: #{binary() => any()}. %% Example: %% filter() :: #{ %% <<"Condition">> => list(any()), %% <<"Key">> => string(), %% <<"Value">> => string() %% } -type filter() :: #{binary() => any()}. %% Example: %% list_explainabilities_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_explainabilities_request() :: #{binary() => any()}. %% Example: %% time_series_condition() :: #{ %% <<"AttributeName">> => string(), %% <<"AttributeValue">> => string(), %% <<"Condition">> => list(any()) %% } -type time_series_condition() :: #{binary() => any()}. %% Example: %% create_explainability_request() :: #{ %% <<"DataSource">> => data_source(), %% <<"EnableVisualization">> => boolean(), %% <<"EndDateTime">> => string(), %% <<"ExplainabilityConfig">> := explainability_config(), %% <<"ExplainabilityName">> := string(), %% <<"ResourceArn">> := string(), %% <<"Schema">> => schema(), %% <<"StartDateTime">> => string(), %% <<"Tags">> => list(tag()) %% } -type create_explainability_request() :: #{binary() => any()}. %% Example: %% delete_predictor_request() :: #{ %% <<"PredictorArn">> := string() %% } -type delete_predictor_request() :: #{binary() => any()}. %% Example: %% metric_result() :: #{ %% <<"MetricName">> => string(), %% <<"MetricValue">> => float() %% } -type metric_result() :: #{binary() => any()}. %% Example: %% data_destination() :: #{ %% <<"S3Config">> => s3_config() %% } -type data_destination() :: #{binary() => any()}. %% Example: %% explainability_config() :: #{ %% <<"TimePointGranularity">> => list(any()), %% <<"TimeSeriesGranularity">> => list(any()) %% } -type explainability_config() :: #{binary() => any()}. %% Example: %% predictor_execution() :: #{ %% <<"AlgorithmArn">> => string(), %% <<"TestWindows">> => list(test_window_summary()) %% } -type predictor_execution() :: #{binary() => any()}. %% Example: %% monitor_data_source() :: #{ %% <<"DatasetImportJobArn">> => string(), %% <<"ForecastArn">> => string(), %% <<"PredictorArn">> => string() %% } -type monitor_data_source() :: #{binary() => any()}. %% Example: %% create_what_if_forecast_export_response() :: #{ %% <<"WhatIfForecastExportArn">> => string() %% } -type create_what_if_forecast_export_response() :: #{binary() => any()}. %% Example: %% action() :: #{ %% <<"AttributeName">> => string(), %% <<"Operation">> => list(any()), %% <<"Value">> => float() %% } -type action() :: #{binary() => any()}. %% Example: %% schema_attribute() :: #{ %% <<"AttributeName">> => string(), %% <<"AttributeType">> => list(any()) %% } -type schema_attribute() :: #{binary() => any()}. %% Example: %% create_explainability_export_response() :: #{ %% <<"ExplainabilityExportArn">> => string() %% } -type create_explainability_export_response() :: #{binary() => any()}. %% Example: %% dataset_import_job_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"DataSource">> => data_source(), %% <<"DatasetImportJobArn">> => string(), %% <<"DatasetImportJobName">> => string(), %% <<"ImportMode">> => list(any()), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string() %% } -type dataset_import_job_summary() :: #{binary() => any()}. %% Example: %% describe_predictor_backtest_export_job_request() :: #{ %% <<"PredictorBacktestExportJobArn">> := string() %% } -type describe_predictor_backtest_export_job_request() :: #{binary() => any()}. %% Example: %% list_dataset_import_jobs_response() :: #{ %% <<"DatasetImportJobs">> => list(dataset_import_job_summary()), %% <<"NextToken">> => string() %% } -type list_dataset_import_jobs_response() :: #{binary() => any()}. %% Example: %% error_metric() :: #{ %% <<"ForecastType">> => string(), %% <<"MAPE">> => float(), %% <<"MASE">> => float(), %% <<"RMSE">> => float(), %% <<"WAPE">> => float() %% } -type error_metric() :: #{binary() => any()}. %% Example: %% delete_dataset_group_request() :: #{ %% <<"DatasetGroupArn">> := string() %% } -type delete_dataset_group_request() :: #{binary() => any()}. %% Example: %% predictor_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"DatasetGroupArn">> => string(), %% <<"IsAutoPredictor">> => boolean(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"PredictorArn">> => string(), %% <<"PredictorName">> => string(), %% <<"ReferencePredictorSummary">> => reference_predictor_summary(), %% <<"Status">> => string() %% } -type predictor_summary() :: #{binary() => any()}. %% Example: %% update_dataset_group_request() :: #{ %% <<"DatasetArns">> := list(string()), %% <<"DatasetGroupArn">> := string() %% } -type update_dataset_group_request() :: #{binary() => any()}. %% Example: %% tag_resource_response() :: #{ %% } -type tag_resource_response() :: #{binary() => any()}. %% Example: %% describe_predictor_request() :: #{ %% <<"PredictorArn">> := string() %% } -type describe_predictor_request() :: #{binary() => any()}. %% Example: %% list_what_if_forecast_exports_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_what_if_forecast_exports_request() :: #{binary() => any()}. %% Example: %% create_forecast_export_job_request() :: #{ %% <<"Destination">> := data_destination(), %% <<"ForecastArn">> := string(), %% <<"ForecastExportJobName">> := string(), %% <<"Format">> => string(), %% <<"Tags">> => list(tag()) %% } -type create_forecast_export_job_request() :: #{binary() => any()}. %% Example: %% create_predictor_backtest_export_job_request() :: #{ %% <<"Destination">> := data_destination(), %% <<"Format">> => string(), %% <<"PredictorArn">> := string(), %% <<"PredictorBacktestExportJobName">> := string(), %% <<"Tags">> => list(tag()) %% } -type create_predictor_backtest_export_job_request() :: #{binary() => any()}. %% Example: %% create_dataset_group_response() :: #{ %% <<"DatasetGroupArn">> => string() %% } -type create_dataset_group_response() :: #{binary() => any()}. %% Example: %% statistics() :: #{ %% <<"Avg">> => float(), %% <<"Count">> => integer(), %% <<"CountDistinct">> => integer(), %% <<"CountDistinctLong">> => float(), %% <<"CountLong">> => float(), %% <<"CountNan">> => integer(), %% <<"CountNanLong">> => float(), %% <<"CountNull">> => integer(), %% <<"CountNullLong">> => float(), %% <<"Max">> => string(), %% <<"Min">> => string(), %% <<"Stddev">> => float() %% } -type statistics() :: #{binary() => any()}. %% Example: %% delete_what_if_forecast_request() :: #{ %% <<"WhatIfForecastArn">> := string() %% } -type delete_what_if_forecast_request() :: #{binary() => any()}. %% Example: %% predictor_backtest_export_job_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"Destination">> => data_destination(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"PredictorBacktestExportJobArn">> => string(), %% <<"PredictorBacktestExportJobName">> => string(), %% <<"Status">> => string() %% } -type predictor_backtest_export_job_summary() :: #{binary() => any()}. %% Example: %% describe_dataset_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"DataFrequency">> => string(), %% <<"DatasetArn">> => string(), %% <<"DatasetName">> => string(), %% <<"DatasetType">> => list(any()), %% <<"Domain">> => list(any()), %% <<"EncryptionConfig">> => encryption_config(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Schema">> => schema(), %% <<"Status">> => string() %% } -type describe_dataset_response() :: #{binary() => any()}. %% Example: %% describe_what_if_forecast_export_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"Destination">> => data_destination(), %% <<"EstimatedTimeRemainingInMinutes">> => float(), %% <<"Format">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string(), %% <<"WhatIfForecastArns">> => list(string()), %% <<"WhatIfForecastExportArn">> => string(), %% <<"WhatIfForecastExportName">> => string() %% } -type describe_what_if_forecast_export_response() :: #{binary() => any()}. %% Example: %% list_tags_for_resource_request() :: #{ %% <<"ResourceArn">> := string() %% } -type list_tags_for_resource_request() :: #{binary() => any()}. %% Example: %% describe_what_if_forecast_request() :: #{ %% <<"WhatIfForecastArn">> := string() %% } -type describe_what_if_forecast_request() :: #{binary() => any()}. %% Example: %% delete_what_if_analysis_request() :: #{ %% <<"WhatIfAnalysisArn">> := string() %% } -type delete_what_if_analysis_request() :: #{binary() => any()}. %% Example: %% create_dataset_request() :: #{ %% <<"DataFrequency">> => string(), %% <<"DatasetName">> := string(), %% <<"DatasetType">> := list(any()), %% <<"Domain">> := list(any()), %% <<"EncryptionConfig">> => encryption_config(), %% <<"Schema">> := schema(), %% <<"Tags">> => list(tag()) %% } -type create_dataset_request() :: #{binary() => any()}. %% Example: %% describe_dataset_group_request() :: #{ %% <<"DatasetGroupArn">> := string() %% } -type describe_dataset_group_request() :: #{binary() => any()}. %% Example: %% delete_predictor_backtest_export_job_request() :: #{ %% <<"PredictorBacktestExportJobArn">> := string() %% } -type delete_predictor_backtest_export_job_request() :: #{binary() => any()}. %% Example: %% continuous_parameter_range() :: #{ %% <<"MaxValue">> => float(), %% <<"MinValue">> => float(), %% <<"Name">> => string(), %% <<"ScalingType">> => list(any()) %% } -type continuous_parameter_range() :: #{binary() => any()}. %% Example: %% create_dataset_import_job_request() :: #{ %% <<"DataSource">> := data_source(), %% <<"DatasetArn">> := string(), %% <<"DatasetImportJobName">> := string(), %% <<"Format">> => string(), %% <<"GeolocationFormat">> => string(), %% <<"ImportMode">> => list(any()), %% <<"Tags">> => list(tag()), %% <<"TimeZone">> => string(), %% <<"TimestampFormat">> => string(), %% <<"UseGeolocationForTimeZone">> => boolean() %% } -type create_dataset_import_job_request() :: #{binary() => any()}. %% Example: %% describe_monitor_request() :: #{ %% <<"MonitorArn">> := string() %% } -type describe_monitor_request() :: #{binary() => any()}. %% Example: %% predictor_event() :: #{ %% <<"Datetime">> => non_neg_integer(), %% <<"Detail">> => string() %% } -type predictor_event() :: #{binary() => any()}. %% Example: %% describe_explainability_request() :: #{ %% <<"ExplainabilityArn">> := string() %% } -type describe_explainability_request() :: #{binary() => any()}. %% Example: %% describe_forecast_export_job_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"Destination">> => data_destination(), %% <<"ForecastArn">> => string(), %% <<"ForecastExportJobArn">> => string(), %% <<"ForecastExportJobName">> => string(), %% <<"Format">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string() %% } -type describe_forecast_export_job_response() :: #{binary() => any()}. %% Example: %% create_explainability_response() :: #{ %% <<"ExplainabilityArn">> => string() %% } -type create_explainability_response() :: #{binary() => any()}. %% Example: %% describe_monitor_response() :: #{ %% <<"Baseline">> => baseline(), %% <<"CreationTime">> => non_neg_integer(), %% <<"EstimatedEvaluationTimeRemainingInMinutes">> => float(), %% <<"LastEvaluationState">> => string(), %% <<"LastEvaluationTime">> => non_neg_integer(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"MonitorArn">> => string(), %% <<"MonitorName">> => string(), %% <<"ResourceArn">> => string(), %% <<"Status">> => string() %% } -type describe_monitor_response() :: #{binary() => any()}. %% Example: %% describe_dataset_import_job_request() :: #{ %% <<"DatasetImportJobArn">> := string() %% } -type describe_dataset_import_job_request() :: #{binary() => any()}. %% Example: %% limit_exceeded_exception() :: #{ %% <<"Message">> => string() %% } -type limit_exceeded_exception() :: #{binary() => any()}. %% Example: %% describe_forecast_export_job_request() :: #{ %% <<"ForecastExportJobArn">> := string() %% } -type describe_forecast_export_job_request() :: #{binary() => any()}. %% Example: %% create_what_if_forecast_export_request() :: #{ %% <<"Destination">> := data_destination(), %% <<"Format">> => string(), %% <<"Tags">> => list(tag()), %% <<"WhatIfForecastArns">> := list(string()), %% <<"WhatIfForecastExportName">> := string() %% } -type create_what_if_forecast_export_request() :: #{binary() => any()}. %% Example: %% what_if_analysis_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"ForecastArn">> => string(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string(), %% <<"WhatIfAnalysisArn">> => string(), %% <<"WhatIfAnalysisName">> => string() %% } -type what_if_analysis_summary() :: #{binary() => any()}. %% Example: %% list_monitor_evaluations_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"MonitorArn">> := string(), %% <<"NextToken">> => string() %% } -type list_monitor_evaluations_request() :: #{binary() => any()}. %% Example: %% describe_dataset_group_response() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"DatasetArns">> => list(string()), %% <<"DatasetGroupArn">> => string(), %% <<"DatasetGroupName">> => string(), %% <<"Domain">> => list(any()), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Status">> => string() %% } -type describe_dataset_group_response() :: #{binary() => any()}. %% Example: %% evaluation_result() :: #{ %% <<"AlgorithmArn">> => string(), %% <<"TestWindows">> => list(window_summary()) %% } -type evaluation_result() :: #{binary() => any()}. %% Example: %% list_forecasts_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_forecasts_request() :: #{binary() => any()}. %% Example: %% describe_dataset_request() :: #{ %% <<"DatasetArn">> := string() %% } -type describe_dataset_request() :: #{binary() => any()}. %% Example: %% delete_dataset_request() :: #{ %% <<"DatasetArn">> := string() %% } -type delete_dataset_request() :: #{binary() => any()}. %% Example: %% list_datasets_response() :: #{ %% <<"Datasets">> => list(dataset_summary()), %% <<"NextToken">> => string() %% } -type list_datasets_response() :: #{binary() => any()}. %% Example: %% what_if_forecast_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"Message">> => string(), %% <<"Status">> => string(), %% <<"WhatIfAnalysisArn">> => string(), %% <<"WhatIfForecastArn">> => string(), %% <<"WhatIfForecastName">> => string() %% } -type what_if_forecast_summary() :: #{binary() => any()}. %% Example: %% create_forecast_request() :: #{ %% <<"ForecastName">> := string(), %% <<"ForecastTypes">> => list(string()), %% <<"PredictorArn">> := string(), %% <<"Tags">> => list(tag()), %% <<"TimeSeriesSelector">> => time_series_selector() %% } -type create_forecast_request() :: #{binary() => any()}. %% Example: %% additional_dataset() :: #{ %% <<"Configuration">> => map(), %% <<"Name">> => string() %% } -type additional_dataset() :: #{binary() => any()}. %% Example: %% delete_explainability_export_request() :: #{ %% <<"ExplainabilityExportArn">> := string() %% } -type delete_explainability_export_request() :: #{binary() => any()}. %% Example: %% resource_already_exists_exception() :: #{ %% <<"Message">> => string() %% } -type resource_already_exists_exception() :: #{binary() => any()}. %% Example: %% create_monitor_request() :: #{ %% <<"MonitorName">> := string(), %% <<"ResourceArn">> := string(), %% <<"Tags">> => list(tag()) %% } -type create_monitor_request() :: #{binary() => any()}. %% Example: %% list_explainability_exports_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_explainability_exports_request() :: #{binary() => any()}. %% Example: %% data_source() :: #{ %% <<"S3Config">> => s3_config() %% } -type data_source() :: #{binary() => any()}. %% Example: %% delete_resource_tree_request() :: #{ %% <<"ResourceArn">> := string() %% } -type delete_resource_tree_request() :: #{binary() => any()}. %% Example: %% window_summary() :: #{ %% <<"EvaluationType">> => list(any()), %% <<"ItemCount">> => integer(), %% <<"Metrics">> => metrics(), %% <<"TestWindowEnd">> => non_neg_integer(), %% <<"TestWindowStart">> => non_neg_integer() %% } -type window_summary() :: #{binary() => any()}. %% Example: %% list_explainabilities_response() :: #{ %% <<"Explainabilities">> => list(explainability_summary()), %% <<"NextToken">> => string() %% } -type list_explainabilities_response() :: #{binary() => any()}. %% Example: %% predictor_baseline() :: #{ %% <<"BaselineMetrics">> => list(baseline_metric()) %% } -type predictor_baseline() :: #{binary() => any()}. %% Example: %% hyper_parameter_tuning_job_config() :: #{ %% <<"ParameterRanges">> => parameter_ranges() %% } -type hyper_parameter_tuning_job_config() :: #{binary() => any()}. %% Example: %% create_forecast_response() :: #{ %% <<"ForecastArn">> => string() %% } -type create_forecast_response() :: #{binary() => any()}. %% Example: %% time_series_replacements_data_source() :: #{ %% <<"Format">> => string(), %% <<"S3Config">> => s3_config(), %% <<"Schema">> => schema(), %% <<"TimestampFormat">> => string() %% } -type time_series_replacements_data_source() :: #{binary() => any()}. %% Example: %% delete_what_if_forecast_export_request() :: #{ %% <<"WhatIfForecastExportArn">> := string() %% } -type delete_what_if_forecast_export_request() :: #{binary() => any()}. %% Example: %% monitor_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"LastModificationTime">> => non_neg_integer(), %% <<"MonitorArn">> => string(), %% <<"MonitorName">> => string(), %% <<"ResourceArn">> => string(), %% <<"Status">> => string() %% } -type monitor_summary() :: #{binary() => any()}. %% Example: %% time_alignment_boundary() :: #{ %% <<"DayOfMonth">> => integer(), %% <<"DayOfWeek">> => list(any()), %% <<"Hour">> => integer(), %% <<"Month">> => list(any()) %% } -type time_alignment_boundary() :: #{binary() => any()}. %% Example: %% featurization() :: #{ %% <<"AttributeName">> => string(), %% <<"FeaturizationPipeline">> => list(featurization_method()) %% } -type featurization() :: #{binary() => any()}. %% Example: %% list_predictor_backtest_export_jobs_request() :: #{ %% <<"Filters">> => list(filter()), %% <<"MaxResults">> => integer(), %% <<"NextToken">> => string() %% } -type list_predictor_backtest_export_jobs_request() :: #{binary() => any()}. %% Example: %% time_series_identifiers() :: #{ %% <<"DataSource">> => data_source(), %% <<"Format">> => string(), %% <<"Schema">> => schema() %% } -type time_series_identifiers() :: #{binary() => any()}. %% Example: %% create_dataset_import_job_response() :: #{ %% <<"DatasetImportJobArn">> => string() %% } -type create_dataset_import_job_response() :: #{binary() => any()}. %% Example: %% dataset_group_summary() :: #{ %% <<"CreationTime">> => non_neg_integer(), %% <<"DatasetGroupArn">> => string(), %% <<"DatasetGroupName">> => string(), %% <<"LastModificationTime">> => non_neg_integer() %% } -type dataset_group_summary() :: #{binary() => any()}. -type create_auto_predictor_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_dataset_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception(). -type create_dataset_group_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_dataset_import_job_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_explainability_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_explainability_export_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_forecast_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_forecast_export_job_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_monitor_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_predictor_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_predictor_backtest_export_job_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_what_if_analysis_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_what_if_forecast_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_what_if_forecast_export_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_dataset_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_dataset_group_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_dataset_import_job_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_explainability_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_explainability_export_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_forecast_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_forecast_export_job_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_monitor_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_predictor_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_predictor_backtest_export_job_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_resource_tree_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_what_if_analysis_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_what_if_forecast_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_what_if_forecast_export_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type describe_auto_predictor_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_dataset_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_dataset_group_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_dataset_import_job_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_explainability_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_explainability_export_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_forecast_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_forecast_export_job_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_monitor_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_predictor_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_predictor_backtest_export_job_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_what_if_analysis_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_what_if_forecast_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_what_if_forecast_export_errors() :: invalid_input_exception() | resource_not_found_exception(). -type get_accuracy_metrics_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type list_dataset_groups_errors() :: invalid_next_token_exception(). -type list_dataset_import_jobs_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_datasets_errors() :: invalid_next_token_exception(). -type list_explainabilities_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_explainability_exports_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_forecast_export_jobs_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_forecasts_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_monitor_evaluations_errors() :: invalid_input_exception() | invalid_next_token_exception() | resource_not_found_exception(). -type list_monitors_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_predictor_backtest_export_jobs_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_predictors_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_tags_for_resource_errors() :: invalid_input_exception() | resource_not_found_exception(). -type list_what_if_analyses_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_what_if_forecast_exports_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_what_if_forecasts_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type resume_resource_errors() :: limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type stop_resource_errors() :: limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception(). -type tag_resource_errors() :: limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception(). -type untag_resource_errors() :: invalid_input_exception() | resource_not_found_exception(). -type update_dataset_group_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). %%==================================================================== %% API %%==================================================================== %% @doc 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'. -spec create_auto_predictor(aws_client:aws_client(), create_auto_predictor_request()) -> {ok, create_auto_predictor_response(), tuple()} | {error, any()} | {error, create_auto_predictor_errors(), tuple()}. create_auto_predictor(Client, Input) when is_map(Client), is_map(Input) -> create_auto_predictor(Client, Input, []). -spec create_auto_predictor(aws_client:aws_client(), create_auto_predictor_request(), proplists:proplist()) -> {ok, create_auto_predictor_response(), tuple()} | {error, any()} | {error, create_auto_predictor_errors(), tuple()}. create_auto_predictor(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateAutoPredictor">>, Input, Options). %% @doc 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: %% https://docs.aws.amazon.com/forecast/latest/dg/howitworks-datasets-groups.html. %% %% To get a list of all your datasets, use the ListDatasets: %% https://docs.aws.amazon.com/forecast/latest/dg/API_ListDatasets.html %% operation. %% %% For example Forecast datasets, see the Amazon Forecast Sample GitHub %% repository: https://github.com/aws-samples/amazon-forecast-samples. %% %% The `Status' of a dataset must be `ACTIVE' before you can import %% training data. Use the DescribeDataset: %% https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDataset.html %% operation to get %% the status. -spec create_dataset(aws_client:aws_client(), create_dataset_request()) -> {ok, create_dataset_response(), tuple()} | {error, any()} | {error, create_dataset_errors(), tuple()}. create_dataset(Client, Input) when is_map(Client), is_map(Input) -> create_dataset(Client, Input, []). -spec create_dataset(aws_client:aws_client(), create_dataset_request(), proplists:proplist()) -> {ok, create_dataset_response(), tuple()} | {error, any()} | {error, create_dataset_errors(), tuple()}. create_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDataset">>, Input, Options). %% @doc 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: %% https://docs.aws.amazon.com/forecast/latest/dg/API_UpdateDatasetGroup.html %% 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: %% https://docs.aws.amazon.com/forecast/latest/dg/howitworks-datasets-groups.html. %% %% To get a list of all your datasets groups, use the ListDatasetGroups: %% https://docs.aws.amazon.com/forecast/latest/dg/API_ListDatasetGroups.html %% 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: %% https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDatasetGroup.html %% operation. -spec create_dataset_group(aws_client:aws_client(), create_dataset_group_request()) -> {ok, create_dataset_group_response(), tuple()} | {error, any()} | {error, create_dataset_group_errors(), tuple()}. create_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_group(Client, Input, []). -spec create_dataset_group(aws_client:aws_client(), create_dataset_group_request(), proplists:proplist()) -> {ok, create_dataset_group_response(), tuple()} | {error, any()} | {error, create_dataset_group_errors(), tuple()}. create_dataset_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDatasetGroup">>, Input, Options). %% @doc 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: %% https://docs.aws.amazon.com/forecast/latest/dg/API_DataSource.html 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: %% https://docs.aws.amazon.com/forecast/latest/dg/aws-forecast-iam-roles.html. %% %% 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: %% https://docs.aws.amazon.com/forecast/latest/dg/API_ListDatasetImportJobs.html %% operation. -spec create_dataset_import_job(aws_client:aws_client(), create_dataset_import_job_request()) -> {ok, create_dataset_import_job_response(), tuple()} | {error, any()} | {error, create_dataset_import_job_errors(), tuple()}. create_dataset_import_job(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_import_job(Client, Input, []). -spec create_dataset_import_job(aws_client:aws_client(), create_dataset_import_job_request(), proplists:proplist()) -> {ok, create_dataset_import_job_response(), tuple()} | {error, any()} | {error, create_dataset_import_job_errors(), tuple()}. create_dataset_import_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDatasetImportJob">>, Input, Options). %% @doc %% 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. -spec create_explainability(aws_client:aws_client(), create_explainability_request()) -> {ok, create_explainability_response(), tuple()} | {error, any()} | {error, create_explainability_errors(), tuple()}. create_explainability(Client, Input) when is_map(Client), is_map(Input) -> create_explainability(Client, Input, []). -spec create_explainability(aws_client:aws_client(), create_explainability_request(), proplists:proplist()) -> {ok, create_explainability_response(), tuple()} | {error, any()} | {error, create_explainability_errors(), tuple()}. create_explainability(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateExplainability">>, Input, Options). %% @doc 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. -spec create_explainability_export(aws_client:aws_client(), create_explainability_export_request()) -> {ok, create_explainability_export_response(), tuple()} | {error, any()} | {error, create_explainability_export_errors(), tuple()}. create_explainability_export(Client, Input) when is_map(Client), is_map(Input) -> create_explainability_export(Client, Input, []). -spec create_explainability_export(aws_client:aws_client(), create_explainability_export_request(), proplists:proplist()) -> {ok, create_explainability_export_response(), tuple()} | {error, any()} | {error, create_explainability_export_errors(), tuple()}. create_explainability_export(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateExplainabilityExport">>, Input, Options). %% @doc 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. -spec create_forecast(aws_client:aws_client(), create_forecast_request()) -> {ok, create_forecast_response(), tuple()} | {error, any()} | {error, create_forecast_errors(), tuple()}. create_forecast(Client, Input) when is_map(Client), is_map(Input) -> create_forecast(Client, Input, []). -spec create_forecast(aws_client:aws_client(), create_forecast_request(), proplists:proplist()) -> {ok, create_forecast_response(), tuple()} | {error, any()} | {error, create_forecast_errors(), tuple()}. create_forecast(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateForecast">>, Input, Options). %% @doc 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. -spec create_forecast_export_job(aws_client:aws_client(), create_forecast_export_job_request()) -> {ok, create_forecast_export_job_response(), tuple()} | {error, any()} | {error, create_forecast_export_job_errors(), tuple()}. create_forecast_export_job(Client, Input) when is_map(Client), is_map(Input) -> create_forecast_export_job(Client, Input, []). -spec create_forecast_export_job(aws_client:aws_client(), create_forecast_export_job_request(), proplists:proplist()) -> {ok, create_forecast_export_job_response(), tuple()} | {error, any()} | {error, create_forecast_export_job_errors(), tuple()}. create_forecast_export_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateForecastExportJob">>, Input, Options). %% @doc 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: %% https://docs.aws.amazon.com/forecast/latest/dg/predictor-monitoring.html. -spec create_monitor(aws_client:aws_client(), create_monitor_request()) -> {ok, create_monitor_response(), tuple()} | {error, any()} | {error, create_monitor_errors(), tuple()}. create_monitor(Client, Input) when is_map(Client), is_map(Input) -> create_monitor(Client, Input, []). -spec create_monitor(aws_client:aws_client(), create_monitor_request(), proplists:proplist()) -> {ok, create_monitor_response(), tuple()} | {error, any()} | {error, create_monitor_errors(), tuple()}. create_monitor(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateMonitor">>, Input, Options). %% @doc %% 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. -spec create_predictor(aws_client:aws_client(), create_predictor_request()) -> {ok, create_predictor_response(), tuple()} | {error, any()} | {error, create_predictor_errors(), tuple()}. create_predictor(Client, Input) when is_map(Client), is_map(Input) -> create_predictor(Client, Input, []). -spec create_predictor(aws_client:aws_client(), create_predictor_request(), proplists:proplist()) -> {ok, create_predictor_response(), tuple()} | {error, any()} | {error, create_predictor_errors(), tuple()}. create_predictor(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreatePredictor">>, Input, Options). %% @doc 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: %% %% `__.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. -spec create_predictor_backtest_export_job(aws_client:aws_client(), create_predictor_backtest_export_job_request()) -> {ok, create_predictor_backtest_export_job_response(), tuple()} | {error, any()} | {error, create_predictor_backtest_export_job_errors(), tuple()}. create_predictor_backtest_export_job(Client, Input) when is_map(Client), is_map(Input) -> create_predictor_backtest_export_job(Client, Input, []). -spec create_predictor_backtest_export_job(aws_client:aws_client(), create_predictor_backtest_export_job_request(), proplists:proplist()) -> {ok, create_predictor_backtest_export_job_response(), tuple()} | {error, any()} | {error, create_predictor_backtest_export_job_errors(), tuple()}. create_predictor_backtest_export_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreatePredictorBacktestExportJob">>, Input, Options). %% @doc 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. -spec create_what_if_analysis(aws_client:aws_client(), create_what_if_analysis_request()) -> {ok, create_what_if_analysis_response(), tuple()} | {error, any()} | {error, create_what_if_analysis_errors(), tuple()}. create_what_if_analysis(Client, Input) when is_map(Client), is_map(Input) -> create_what_if_analysis(Client, Input, []). -spec create_what_if_analysis(aws_client:aws_client(), create_what_if_analysis_request(), proplists:proplist()) -> {ok, create_what_if_analysis_response(), tuple()} | {error, any()} | {error, create_what_if_analysis_errors(), tuple()}. create_what_if_analysis(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateWhatIfAnalysis">>, Input, Options). %% @doc 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. -spec create_what_if_forecast(aws_client:aws_client(), create_what_if_forecast_request()) -> {ok, create_what_if_forecast_response(), tuple()} | {error, any()} | {error, create_what_if_forecast_errors(), tuple()}. create_what_if_forecast(Client, Input) when is_map(Client), is_map(Input) -> create_what_if_forecast(Client, Input, []). -spec create_what_if_forecast(aws_client:aws_client(), create_what_if_forecast_request(), proplists:proplist()) -> {ok, create_what_if_forecast_response(), tuple()} | {error, any()} | {error, create_what_if_forecast_errors(), tuple()}. create_what_if_forecast(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateWhatIfForecast">>, Input, Options). %% @doc 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. -spec create_what_if_forecast_export(aws_client:aws_client(), create_what_if_forecast_export_request()) -> {ok, create_what_if_forecast_export_response(), tuple()} | {error, any()} | {error, create_what_if_forecast_export_errors(), tuple()}. create_what_if_forecast_export(Client, Input) when is_map(Client), is_map(Input) -> create_what_if_forecast_export(Client, Input, []). -spec create_what_if_forecast_export(aws_client:aws_client(), create_what_if_forecast_export_request(), proplists:proplist()) -> {ok, create_what_if_forecast_export_response(), tuple()} | {error, any()} | {error, create_what_if_forecast_export_errors(), tuple()}. create_what_if_forecast_export(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateWhatIfForecastExport">>, Input, Options). %% @doc Deletes an Amazon Forecast dataset that was created using the %% CreateDataset: %% https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDataset.html %% operation. %% %% You can %% only delete datasets that have a status of `ACTIVE' or %% `CREATE_FAILED'. %% To get the status use the DescribeDataset: %% https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDataset.html %% operation. %% %% Forecast does not automatically update any dataset groups that contain the %% deleted dataset. %% In order to update the dataset group, use the UpdateDatasetGroup: %% https://docs.aws.amazon.com/forecast/latest/dg/API_UpdateDatasetGroup.html %% operation, %% omitting the deleted dataset's ARN. -spec delete_dataset(aws_client:aws_client(), delete_dataset_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_dataset_errors(), tuple()}. delete_dataset(Client, Input) when is_map(Client), is_map(Input) -> delete_dataset(Client, Input, []). -spec delete_dataset(aws_client:aws_client(), delete_dataset_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_dataset_errors(), tuple()}. delete_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteDataset">>, Input, Options). %% @doc Deletes a dataset group created using the CreateDatasetGroup: %% https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDatasetGroup.html %% 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: %% https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDatasetGroup.html %% operation. %% %% This operation deletes only the dataset group, not the datasets in the %% group. -spec delete_dataset_group(aws_client:aws_client(), delete_dataset_group_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_dataset_group_errors(), tuple()}. delete_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> delete_dataset_group(Client, Input, []). -spec delete_dataset_group(aws_client:aws_client(), delete_dataset_group_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_dataset_group_errors(), tuple()}. delete_dataset_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteDatasetGroup">>, Input, Options). %% @doc Deletes a dataset import job created using the %% CreateDatasetImportJob: %% https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDatasetImportJob.html %% operation. %% %% You can delete only dataset import jobs that have a status of `ACTIVE' %% or `CREATE_FAILED'. To get the status, use the %% DescribeDatasetImportJob: %% https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDatasetImportJob.html %% operation. -spec delete_dataset_import_job(aws_client:aws_client(), delete_dataset_import_job_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_dataset_import_job_errors(), tuple()}. delete_dataset_import_job(Client, Input) when is_map(Client), is_map(Input) -> delete_dataset_import_job(Client, Input, []). -spec delete_dataset_import_job(aws_client:aws_client(), delete_dataset_import_job_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_dataset_import_job_errors(), tuple()}. delete_dataset_import_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteDatasetImportJob">>, Input, Options). %% @doc 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. -spec delete_explainability(aws_client:aws_client(), delete_explainability_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_explainability_errors(), tuple()}. delete_explainability(Client, Input) when is_map(Client), is_map(Input) -> delete_explainability(Client, Input, []). -spec delete_explainability(aws_client:aws_client(), delete_explainability_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_explainability_errors(), tuple()}. delete_explainability(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteExplainability">>, Input, Options). %% @doc Deletes an Explainability export. -spec delete_explainability_export(aws_client:aws_client(), delete_explainability_export_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_explainability_export_errors(), tuple()}. delete_explainability_export(Client, Input) when is_map(Client), is_map(Input) -> delete_explainability_export(Client, Input, []). -spec delete_explainability_export(aws_client:aws_client(), delete_explainability_export_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_explainability_export_errors(), tuple()}. delete_explainability_export(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteExplainabilityExport">>, Input, Options). %% @doc 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. -spec delete_forecast(aws_client:aws_client(), delete_forecast_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_forecast_errors(), tuple()}. delete_forecast(Client, Input) when is_map(Client), is_map(Input) -> delete_forecast(Client, Input, []). -spec delete_forecast(aws_client:aws_client(), delete_forecast_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_forecast_errors(), tuple()}. delete_forecast(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteForecast">>, Input, Options). %% @doc 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. -spec delete_forecast_export_job(aws_client:aws_client(), delete_forecast_export_job_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_forecast_export_job_errors(), tuple()}. delete_forecast_export_job(Client, Input) when is_map(Client), is_map(Input) -> delete_forecast_export_job(Client, Input, []). -spec delete_forecast_export_job(aws_client:aws_client(), delete_forecast_export_job_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_forecast_export_job_errors(), tuple()}. delete_forecast_export_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteForecastExportJob">>, Input, Options). %% @doc Deletes a monitor resource. %% %% You can only delete a monitor resource with a status of `ACTIVE', %% `ACTIVE_STOPPED', `CREATE_FAILED', or `CREATE_STOPPED'. -spec delete_monitor(aws_client:aws_client(), delete_monitor_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_monitor_errors(), tuple()}. delete_monitor(Client, Input) when is_map(Client), is_map(Input) -> delete_monitor(Client, Input, []). -spec delete_monitor(aws_client:aws_client(), delete_monitor_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_monitor_errors(), tuple()}. delete_monitor(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteMonitor">>, Input, Options). %% @doc 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. -spec delete_predictor(aws_client:aws_client(), delete_predictor_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_predictor_errors(), tuple()}. delete_predictor(Client, Input) when is_map(Client), is_map(Input) -> delete_predictor(Client, Input, []). -spec delete_predictor(aws_client:aws_client(), delete_predictor_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_predictor_errors(), tuple()}. delete_predictor(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeletePredictor">>, Input, Options). %% @doc Deletes a predictor backtest export job. -spec delete_predictor_backtest_export_job(aws_client:aws_client(), delete_predictor_backtest_export_job_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_predictor_backtest_export_job_errors(), tuple()}. delete_predictor_backtest_export_job(Client, Input) when is_map(Client), is_map(Input) -> delete_predictor_backtest_export_job(Client, Input, []). -spec delete_predictor_backtest_export_job(aws_client:aws_client(), delete_predictor_backtest_export_job_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_predictor_backtest_export_job_errors(), tuple()}. delete_predictor_backtest_export_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeletePredictorBacktestExportJob">>, Input, Options). %% @doc 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. -spec delete_resource_tree(aws_client:aws_client(), delete_resource_tree_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_resource_tree_errors(), tuple()}. delete_resource_tree(Client, Input) when is_map(Client), is_map(Input) -> delete_resource_tree(Client, Input, []). -spec delete_resource_tree(aws_client:aws_client(), delete_resource_tree_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_resource_tree_errors(), tuple()}. delete_resource_tree(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteResourceTree">>, Input, Options). %% @doc 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. -spec delete_what_if_analysis(aws_client:aws_client(), delete_what_if_analysis_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_what_if_analysis_errors(), tuple()}. delete_what_if_analysis(Client, Input) when is_map(Client), is_map(Input) -> delete_what_if_analysis(Client, Input, []). -spec delete_what_if_analysis(aws_client:aws_client(), delete_what_if_analysis_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_what_if_analysis_errors(), tuple()}. delete_what_if_analysis(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteWhatIfAnalysis">>, Input, Options). %% @doc 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. -spec delete_what_if_forecast(aws_client:aws_client(), delete_what_if_forecast_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_what_if_forecast_errors(), tuple()}. delete_what_if_forecast(Client, Input) when is_map(Client), is_map(Input) -> delete_what_if_forecast(Client, Input, []). -spec delete_what_if_forecast(aws_client:aws_client(), delete_what_if_forecast_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_what_if_forecast_errors(), tuple()}. delete_what_if_forecast(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteWhatIfForecast">>, Input, Options). %% @doc 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. -spec delete_what_if_forecast_export(aws_client:aws_client(), delete_what_if_forecast_export_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_what_if_forecast_export_errors(), tuple()}. delete_what_if_forecast_export(Client, Input) when is_map(Client), is_map(Input) -> delete_what_if_forecast_export(Client, Input, []). -spec delete_what_if_forecast_export(aws_client:aws_client(), delete_what_if_forecast_export_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_what_if_forecast_export_errors(), tuple()}. delete_what_if_forecast_export(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteWhatIfForecastExport">>, Input, Options). %% @doc Describes a predictor created using the CreateAutoPredictor %% operation. -spec describe_auto_predictor(aws_client:aws_client(), describe_auto_predictor_request()) -> {ok, describe_auto_predictor_response(), tuple()} | {error, any()} | {error, describe_auto_predictor_errors(), tuple()}. describe_auto_predictor(Client, Input) when is_map(Client), is_map(Input) -> describe_auto_predictor(Client, Input, []). -spec describe_auto_predictor(aws_client:aws_client(), describe_auto_predictor_request(), proplists:proplist()) -> {ok, describe_auto_predictor_response(), tuple()} | {error, any()} | {error, describe_auto_predictor_errors(), tuple()}. describe_auto_predictor(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeAutoPredictor">>, Input, Options). %% @doc Describes an Amazon Forecast dataset created using the CreateDataset: %% https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDataset.html %% operation. %% %% In addition to listing the parameters specified in the `CreateDataset' %% request, %% this operation includes the following dataset properties: %% %% `CreationTime' %% %% `LastModificationTime' %% %% `Status' -spec describe_dataset(aws_client:aws_client(), describe_dataset_request()) -> {ok, describe_dataset_response(), tuple()} | {error, any()} | {error, describe_dataset_errors(), tuple()}. describe_dataset(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset(Client, Input, []). -spec describe_dataset(aws_client:aws_client(), describe_dataset_request(), proplists:proplist()) -> {ok, describe_dataset_response(), tuple()} | {error, any()} | {error, describe_dataset_errors(), tuple()}. describe_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDataset">>, Input, Options). %% @doc Describes a dataset group created using the CreateDatasetGroup: %% https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDatasetGroup.html %% 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' -spec describe_dataset_group(aws_client:aws_client(), describe_dataset_group_request()) -> {ok, describe_dataset_group_response(), tuple()} | {error, any()} | {error, describe_dataset_group_errors(), tuple()}. describe_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_group(Client, Input, []). -spec describe_dataset_group(aws_client:aws_client(), describe_dataset_group_request(), proplists:proplist()) -> {ok, describe_dataset_group_response(), tuple()} | {error, any()} | {error, describe_dataset_group_errors(), tuple()}. describe_dataset_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDatasetGroup">>, Input, Options). %% @doc Describes a dataset import job created using the %% CreateDatasetImportJob: %% https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDatasetImportJob.html %% 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. -spec describe_dataset_import_job(aws_client:aws_client(), describe_dataset_import_job_request()) -> {ok, describe_dataset_import_job_response(), tuple()} | {error, any()} | {error, describe_dataset_import_job_errors(), tuple()}. describe_dataset_import_job(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_import_job(Client, Input, []). -spec describe_dataset_import_job(aws_client:aws_client(), describe_dataset_import_job_request(), proplists:proplist()) -> {ok, describe_dataset_import_job_response(), tuple()} | {error, any()} | {error, describe_dataset_import_job_errors(), tuple()}. describe_dataset_import_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDatasetImportJob">>, Input, Options). %% @doc Describes an Explainability resource created using the %% `CreateExplainability' operation. -spec describe_explainability(aws_client:aws_client(), describe_explainability_request()) -> {ok, describe_explainability_response(), tuple()} | {error, any()} | {error, describe_explainability_errors(), tuple()}. describe_explainability(Client, Input) when is_map(Client), is_map(Input) -> describe_explainability(Client, Input, []). -spec describe_explainability(aws_client:aws_client(), describe_explainability_request(), proplists:proplist()) -> {ok, describe_explainability_response(), tuple()} | {error, any()} | {error, describe_explainability_errors(), tuple()}. describe_explainability(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeExplainability">>, Input, Options). %% @doc Describes an Explainability export created using the %% `CreateExplainabilityExport' operation. -spec describe_explainability_export(aws_client:aws_client(), describe_explainability_export_request()) -> {ok, describe_explainability_export_response(), tuple()} | {error, any()} | {error, describe_explainability_export_errors(), tuple()}. describe_explainability_export(Client, Input) when is_map(Client), is_map(Input) -> describe_explainability_export(Client, Input, []). -spec describe_explainability_export(aws_client:aws_client(), describe_explainability_export_request(), proplists:proplist()) -> {ok, describe_explainability_export_response(), tuple()} | {error, any()} | {error, describe_explainability_export_errors(), tuple()}. describe_explainability_export(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeExplainabilityExport">>, Input, Options). %% @doc 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. -spec describe_forecast(aws_client:aws_client(), describe_forecast_request()) -> {ok, describe_forecast_response(), tuple()} | {error, any()} | {error, describe_forecast_errors(), tuple()}. describe_forecast(Client, Input) when is_map(Client), is_map(Input) -> describe_forecast(Client, Input, []). -spec describe_forecast(aws_client:aws_client(), describe_forecast_request(), proplists:proplist()) -> {ok, describe_forecast_response(), tuple()} | {error, any()} | {error, describe_forecast_errors(), tuple()}. describe_forecast(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeForecast">>, Input, Options). %% @doc 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. -spec describe_forecast_export_job(aws_client:aws_client(), describe_forecast_export_job_request()) -> {ok, describe_forecast_export_job_response(), tuple()} | {error, any()} | {error, describe_forecast_export_job_errors(), tuple()}. describe_forecast_export_job(Client, Input) when is_map(Client), is_map(Input) -> describe_forecast_export_job(Client, Input, []). -spec describe_forecast_export_job(aws_client:aws_client(), describe_forecast_export_job_request(), proplists:proplist()) -> {ok, describe_forecast_export_job_response(), tuple()} | {error, any()} | {error, describe_forecast_export_job_errors(), tuple()}. describe_forecast_export_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeForecastExportJob">>, Input, Options). %% @doc 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' -spec describe_monitor(aws_client:aws_client(), describe_monitor_request()) -> {ok, describe_monitor_response(), tuple()} | {error, any()} | {error, describe_monitor_errors(), tuple()}. describe_monitor(Client, Input) when is_map(Client), is_map(Input) -> describe_monitor(Client, Input, []). -spec describe_monitor(aws_client:aws_client(), describe_monitor_request(), proplists:proplist()) -> {ok, describe_monitor_response(), tuple()} | {error, any()} | {error, describe_monitor_errors(), tuple()}. describe_monitor(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeMonitor">>, Input, Options). %% @doc %% 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. -spec describe_predictor(aws_client:aws_client(), describe_predictor_request()) -> {ok, describe_predictor_response(), tuple()} | {error, any()} | {error, describe_predictor_errors(), tuple()}. describe_predictor(Client, Input) when is_map(Client), is_map(Input) -> describe_predictor(Client, Input, []). -spec describe_predictor(aws_client:aws_client(), describe_predictor_request(), proplists:proplist()) -> {ok, describe_predictor_response(), tuple()} | {error, any()} | {error, describe_predictor_errors(), tuple()}. describe_predictor(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribePredictor">>, Input, Options). %% @doc 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) -spec describe_predictor_backtest_export_job(aws_client:aws_client(), describe_predictor_backtest_export_job_request()) -> {ok, describe_predictor_backtest_export_job_response(), tuple()} | {error, any()} | {error, describe_predictor_backtest_export_job_errors(), tuple()}. describe_predictor_backtest_export_job(Client, Input) when is_map(Client), is_map(Input) -> describe_predictor_backtest_export_job(Client, Input, []). -spec describe_predictor_backtest_export_job(aws_client:aws_client(), describe_predictor_backtest_export_job_request(), proplists:proplist()) -> {ok, describe_predictor_backtest_export_job_response(), tuple()} | {error, any()} | {error, describe_predictor_backtest_export_job_errors(), tuple()}. describe_predictor_backtest_export_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribePredictorBacktestExportJob">>, Input, Options). %% @doc 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' -spec describe_what_if_analysis(aws_client:aws_client(), describe_what_if_analysis_request()) -> {ok, describe_what_if_analysis_response(), tuple()} | {error, any()} | {error, describe_what_if_analysis_errors(), tuple()}. describe_what_if_analysis(Client, Input) when is_map(Client), is_map(Input) -> describe_what_if_analysis(Client, Input, []). -spec describe_what_if_analysis(aws_client:aws_client(), describe_what_if_analysis_request(), proplists:proplist()) -> {ok, describe_what_if_analysis_response(), tuple()} | {error, any()} | {error, describe_what_if_analysis_errors(), tuple()}. describe_what_if_analysis(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeWhatIfAnalysis">>, Input, Options). %% @doc 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' -spec describe_what_if_forecast(aws_client:aws_client(), describe_what_if_forecast_request()) -> {ok, describe_what_if_forecast_response(), tuple()} | {error, any()} | {error, describe_what_if_forecast_errors(), tuple()}. describe_what_if_forecast(Client, Input) when is_map(Client), is_map(Input) -> describe_what_if_forecast(Client, Input, []). -spec describe_what_if_forecast(aws_client:aws_client(), describe_what_if_forecast_request(), proplists:proplist()) -> {ok, describe_what_if_forecast_response(), tuple()} | {error, any()} | {error, describe_what_if_forecast_errors(), tuple()}. describe_what_if_forecast(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeWhatIfForecast">>, Input, Options). %% @doc 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' -spec describe_what_if_forecast_export(aws_client:aws_client(), describe_what_if_forecast_export_request()) -> {ok, describe_what_if_forecast_export_response(), tuple()} | {error, any()} | {error, describe_what_if_forecast_export_errors(), tuple()}. describe_what_if_forecast_export(Client, Input) when is_map(Client), is_map(Input) -> describe_what_if_forecast_export(Client, Input, []). -spec describe_what_if_forecast_export(aws_client:aws_client(), describe_what_if_forecast_export_request(), proplists:proplist()) -> {ok, describe_what_if_forecast_export_response(), tuple()} | {error, any()} | {error, describe_what_if_forecast_export_errors(), tuple()}. describe_what_if_forecast_export(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeWhatIfForecastExport">>, Input, Options). %% @doc 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 (`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. -spec get_accuracy_metrics(aws_client:aws_client(), get_accuracy_metrics_request()) -> {ok, get_accuracy_metrics_response(), tuple()} | {error, any()} | {error, get_accuracy_metrics_errors(), tuple()}. get_accuracy_metrics(Client, Input) when is_map(Client), is_map(Input) -> get_accuracy_metrics(Client, Input, []). -spec get_accuracy_metrics(aws_client:aws_client(), get_accuracy_metrics_request(), proplists:proplist()) -> {ok, get_accuracy_metrics_response(), tuple()} | {error, any()} | {error, get_accuracy_metrics_errors(), tuple()}. get_accuracy_metrics(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"GetAccuracyMetrics">>, Input, Options). %% @doc Returns a list of dataset groups created using the %% CreateDatasetGroup: %% https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDatasetGroup.html %% 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: %% https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDatasetGroup.html %% operation. -spec list_dataset_groups(aws_client:aws_client(), list_dataset_groups_request()) -> {ok, list_dataset_groups_response(), tuple()} | {error, any()} | {error, list_dataset_groups_errors(), tuple()}. list_dataset_groups(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_groups(Client, Input, []). -spec list_dataset_groups(aws_client:aws_client(), list_dataset_groups_request(), proplists:proplist()) -> {ok, list_dataset_groups_response(), tuple()} | {error, any()} | {error, list_dataset_groups_errors(), tuple()}. list_dataset_groups(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasetGroups">>, Input, Options). %% @doc Returns a list of dataset import jobs created using the %% CreateDatasetImportJob: %% https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDatasetImportJob.html %% 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: %% https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDatasetImportJob.html %% operation. You can filter the list by providing an array of Filter: %% https://docs.aws.amazon.com/forecast/latest/dg/API_Filter.html objects. -spec list_dataset_import_jobs(aws_client:aws_client(), list_dataset_import_jobs_request()) -> {ok, list_dataset_import_jobs_response(), tuple()} | {error, any()} | {error, list_dataset_import_jobs_errors(), tuple()}. list_dataset_import_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_import_jobs(Client, Input, []). -spec list_dataset_import_jobs(aws_client:aws_client(), list_dataset_import_jobs_request(), proplists:proplist()) -> {ok, list_dataset_import_jobs_response(), tuple()} | {error, any()} | {error, list_dataset_import_jobs_errors(), tuple()}. list_dataset_import_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasetImportJobs">>, Input, Options). %% @doc Returns a list of datasets created using the CreateDataset: %% https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDataset.html %% 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: %% https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDataset.html %% operation. -spec list_datasets(aws_client:aws_client(), list_datasets_request()) -> {ok, list_datasets_response(), tuple()} | {error, any()} | {error, list_datasets_errors(), tuple()}. list_datasets(Client, Input) when is_map(Client), is_map(Input) -> list_datasets(Client, Input, []). -spec list_datasets(aws_client:aws_client(), list_datasets_request(), proplists:proplist()) -> {ok, list_datasets_response(), tuple()} | {error, any()} | {error, list_datasets_errors(), tuple()}. list_datasets(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasets">>, Input, Options). %% @doc 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. -spec list_explainabilities(aws_client:aws_client(), list_explainabilities_request()) -> {ok, list_explainabilities_response(), tuple()} | {error, any()} | {error, list_explainabilities_errors(), tuple()}. list_explainabilities(Client, Input) when is_map(Client), is_map(Input) -> list_explainabilities(Client, Input, []). -spec list_explainabilities(aws_client:aws_client(), list_explainabilities_request(), proplists:proplist()) -> {ok, list_explainabilities_response(), tuple()} | {error, any()} | {error, list_explainabilities_errors(), tuple()}. list_explainabilities(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListExplainabilities">>, Input, Options). %% @doc 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. -spec list_explainability_exports(aws_client:aws_client(), list_explainability_exports_request()) -> {ok, list_explainability_exports_response(), tuple()} | {error, any()} | {error, list_explainability_exports_errors(), tuple()}. list_explainability_exports(Client, Input) when is_map(Client), is_map(Input) -> list_explainability_exports(Client, Input, []). -spec list_explainability_exports(aws_client:aws_client(), list_explainability_exports_request(), proplists:proplist()) -> {ok, list_explainability_exports_response(), tuple()} | {error, any()} | {error, list_explainability_exports_errors(), tuple()}. list_explainability_exports(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListExplainabilityExports">>, Input, Options). %% @doc 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. -spec list_forecast_export_jobs(aws_client:aws_client(), list_forecast_export_jobs_request()) -> {ok, list_forecast_export_jobs_response(), tuple()} | {error, any()} | {error, list_forecast_export_jobs_errors(), tuple()}. list_forecast_export_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_forecast_export_jobs(Client, Input, []). -spec list_forecast_export_jobs(aws_client:aws_client(), list_forecast_export_jobs_request(), proplists:proplist()) -> {ok, list_forecast_export_jobs_response(), tuple()} | {error, any()} | {error, list_forecast_export_jobs_errors(), tuple()}. list_forecast_export_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListForecastExportJobs">>, Input, Options). %% @doc 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. -spec list_forecasts(aws_client:aws_client(), list_forecasts_request()) -> {ok, list_forecasts_response(), tuple()} | {error, any()} | {error, list_forecasts_errors(), tuple()}. list_forecasts(Client, Input) when is_map(Client), is_map(Input) -> list_forecasts(Client, Input, []). -spec list_forecasts(aws_client:aws_client(), list_forecasts_request(), proplists:proplist()) -> {ok, list_forecasts_response(), tuple()} | {error, any()} | {error, list_forecasts_errors(), tuple()}. list_forecasts(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListForecasts">>, Input, Options). %% @doc 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: %% https://docs.aws.amazon.com/forecast/latest/dg/predictor-monitoring-results.html. -spec list_monitor_evaluations(aws_client:aws_client(), list_monitor_evaluations_request()) -> {ok, list_monitor_evaluations_response(), tuple()} | {error, any()} | {error, list_monitor_evaluations_errors(), tuple()}. list_monitor_evaluations(Client, Input) when is_map(Client), is_map(Input) -> list_monitor_evaluations(Client, Input, []). -spec list_monitor_evaluations(aws_client:aws_client(), list_monitor_evaluations_request(), proplists:proplist()) -> {ok, list_monitor_evaluations_response(), tuple()} | {error, any()} | {error, list_monitor_evaluations_errors(), tuple()}. list_monitor_evaluations(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListMonitorEvaluations">>, Input, Options). %% @doc 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. -spec list_monitors(aws_client:aws_client(), list_monitors_request()) -> {ok, list_monitors_response(), tuple()} | {error, any()} | {error, list_monitors_errors(), tuple()}. list_monitors(Client, Input) when is_map(Client), is_map(Input) -> list_monitors(Client, Input, []). -spec list_monitors(aws_client:aws_client(), list_monitors_request(), proplists:proplist()) -> {ok, list_monitors_response(), tuple()} | {error, any()} | {error, list_monitors_errors(), tuple()}. list_monitors(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListMonitors">>, Input, Options). %% @doc 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. -spec list_predictor_backtest_export_jobs(aws_client:aws_client(), list_predictor_backtest_export_jobs_request()) -> {ok, list_predictor_backtest_export_jobs_response(), tuple()} | {error, any()} | {error, list_predictor_backtest_export_jobs_errors(), tuple()}. list_predictor_backtest_export_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_predictor_backtest_export_jobs(Client, Input, []). -spec list_predictor_backtest_export_jobs(aws_client:aws_client(), list_predictor_backtest_export_jobs_request(), proplists:proplist()) -> {ok, list_predictor_backtest_export_jobs_response(), tuple()} | {error, any()} | {error, list_predictor_backtest_export_jobs_errors(), tuple()}. list_predictor_backtest_export_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListPredictorBacktestExportJobs">>, Input, Options). %% @doc 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. -spec list_predictors(aws_client:aws_client(), list_predictors_request()) -> {ok, list_predictors_response(), tuple()} | {error, any()} | {error, list_predictors_errors(), tuple()}. list_predictors(Client, Input) when is_map(Client), is_map(Input) -> list_predictors(Client, Input, []). -spec list_predictors(aws_client:aws_client(), list_predictors_request(), proplists:proplist()) -> {ok, list_predictors_response(), tuple()} | {error, any()} | {error, list_predictors_errors(), tuple()}. list_predictors(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListPredictors">>, Input, Options). %% @doc Lists the tags for an Amazon Forecast resource. -spec list_tags_for_resource(aws_client:aws_client(), list_tags_for_resource_request()) -> {ok, list_tags_for_resource_response(), tuple()} | {error, any()} | {error, list_tags_for_resource_errors(), tuple()}. list_tags_for_resource(Client, Input) when is_map(Client), is_map(Input) -> list_tags_for_resource(Client, Input, []). -spec list_tags_for_resource(aws_client:aws_client(), list_tags_for_resource_request(), proplists:proplist()) -> {ok, list_tags_for_resource_response(), tuple()} | {error, any()} | {error, list_tags_for_resource_errors(), tuple()}. list_tags_for_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListTagsForResource">>, Input, Options). %% @doc 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. -spec list_what_if_analyses(aws_client:aws_client(), list_what_if_analyses_request()) -> {ok, list_what_if_analyses_response(), tuple()} | {error, any()} | {error, list_what_if_analyses_errors(), tuple()}. list_what_if_analyses(Client, Input) when is_map(Client), is_map(Input) -> list_what_if_analyses(Client, Input, []). -spec list_what_if_analyses(aws_client:aws_client(), list_what_if_analyses_request(), proplists:proplist()) -> {ok, list_what_if_analyses_response(), tuple()} | {error, any()} | {error, list_what_if_analyses_errors(), tuple()}. list_what_if_analyses(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListWhatIfAnalyses">>, Input, Options). %% @doc 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. -spec list_what_if_forecast_exports(aws_client:aws_client(), list_what_if_forecast_exports_request()) -> {ok, list_what_if_forecast_exports_response(), tuple()} | {error, any()} | {error, list_what_if_forecast_exports_errors(), tuple()}. list_what_if_forecast_exports(Client, Input) when is_map(Client), is_map(Input) -> list_what_if_forecast_exports(Client, Input, []). -spec list_what_if_forecast_exports(aws_client:aws_client(), list_what_if_forecast_exports_request(), proplists:proplist()) -> {ok, list_what_if_forecast_exports_response(), tuple()} | {error, any()} | {error, list_what_if_forecast_exports_errors(), tuple()}. list_what_if_forecast_exports(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListWhatIfForecastExports">>, Input, Options). %% @doc 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. -spec list_what_if_forecasts(aws_client:aws_client(), list_what_if_forecasts_request()) -> {ok, list_what_if_forecasts_response(), tuple()} | {error, any()} | {error, list_what_if_forecasts_errors(), tuple()}. list_what_if_forecasts(Client, Input) when is_map(Client), is_map(Input) -> list_what_if_forecasts(Client, Input, []). -spec list_what_if_forecasts(aws_client:aws_client(), list_what_if_forecasts_request(), proplists:proplist()) -> {ok, list_what_if_forecasts_response(), tuple()} | {error, any()} | {error, list_what_if_forecasts_errors(), tuple()}. list_what_if_forecasts(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListWhatIfForecasts">>, Input, Options). %% @doc Resumes a stopped monitor resource. -spec resume_resource(aws_client:aws_client(), resume_resource_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, resume_resource_errors(), tuple()}. resume_resource(Client, Input) when is_map(Client), is_map(Input) -> resume_resource(Client, Input, []). -spec resume_resource(aws_client:aws_client(), resume_resource_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, resume_resource_errors(), tuple()}. resume_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ResumeResource">>, Input, Options). %% @doc 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 -spec stop_resource(aws_client:aws_client(), stop_resource_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, stop_resource_errors(), tuple()}. stop_resource(Client, Input) when is_map(Client), is_map(Input) -> stop_resource(Client, Input, []). -spec stop_resource(aws_client:aws_client(), stop_resource_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, stop_resource_errors(), tuple()}. stop_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopResource">>, Input, Options). %% @doc 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. -spec tag_resource(aws_client:aws_client(), tag_resource_request()) -> {ok, tag_resource_response(), tuple()} | {error, any()} | {error, tag_resource_errors(), tuple()}. tag_resource(Client, Input) when is_map(Client), is_map(Input) -> tag_resource(Client, Input, []). -spec tag_resource(aws_client:aws_client(), tag_resource_request(), proplists:proplist()) -> {ok, tag_resource_response(), tuple()} | {error, any()} | {error, tag_resource_errors(), tuple()}. tag_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"TagResource">>, Input, Options). %% @doc Deletes the specified tags from a resource. -spec untag_resource(aws_client:aws_client(), untag_resource_request()) -> {ok, untag_resource_response(), tuple()} | {error, any()} | {error, untag_resource_errors(), tuple()}. untag_resource(Client, Input) when is_map(Client), is_map(Input) -> untag_resource(Client, Input, []). -spec untag_resource(aws_client:aws_client(), untag_resource_request(), proplists:proplist()) -> {ok, untag_resource_response(), tuple()} | {error, any()} | {error, untag_resource_errors(), tuple()}. untag_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UntagResource">>, Input, Options). %% @doc 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: %% https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDatasetGroup.html %% operation to get the status. -spec update_dataset_group(aws_client:aws_client(), update_dataset_group_request()) -> {ok, update_dataset_group_response(), tuple()} | {error, any()} | {error, update_dataset_group_errors(), tuple()}. update_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> update_dataset_group(Client, Input, []). -spec update_dataset_group(aws_client:aws_client(), update_dataset_group_request(), proplists:proplist()) -> {ok, update_dataset_group_response(), tuple()} | {error, any()} | {error, update_dataset_group_errors(), tuple()}. update_dataset_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateDatasetGroup">>, Input, Options). %%==================================================================== %% Internal functions %%==================================================================== -spec request(aws_client:aws_client(), binary(), map(), list()) -> {ok, Result, {integer(), list(), hackney:client()}} | {error, Error, {integer(), list(), hackney:client()}} | {error, term()} when Result :: map() | undefined, Error :: map(). request(Client, Action, Input, Options) -> RequestFun = fun() -> do_request(Client, Action, Input, Options) end, aws_request:request(RequestFun, Options). do_request(Client, Action, Input0, Options) -> Client1 = Client#{service => <<"forecast">>}, Host = build_host(<<"forecast">>, Client1), URL = build_url(Host, Client1), Headers = [ {<<"Host">>, Host}, {<<"Content-Type">>, <<"application/x-amz-json-1.1">>}, {<<"X-Amz-Target">>, <<"AmazonForecast.", Action/binary>>} ], Input = Input0, Payload = jsx:encode(Input), SignedHeaders = aws_request:sign_request(Client1, <<"POST">>, URL, Headers, Payload), Response = hackney:request(post, URL, SignedHeaders, Payload, Options), handle_response(Response). handle_response({ok, 200, ResponseHeaders, Client}) -> case hackney:body(Client) of {ok, <<>>} -> {ok, undefined, {200, ResponseHeaders, Client}}; {ok, Body} -> Result = jsx:decode(Body), {ok, Result, {200, ResponseHeaders, Client}} end; handle_response({ok, StatusCode, ResponseHeaders, Client}) -> {ok, Body} = hackney:body(Client), Error = jsx:decode(Body), {error, Error, {StatusCode, ResponseHeaders, Client}}; handle_response({error, Reason}) -> {error, Reason}. build_host(_EndpointPrefix, #{region := <<"local">>, endpoint := Endpoint}) -> Endpoint; build_host(_EndpointPrefix, #{region := <<"local">>}) -> <<"localhost">>; build_host(EndpointPrefix, #{region := Region, endpoint := Endpoint}) -> aws_util:binary_join([EndpointPrefix, Region, Endpoint], <<".">>). build_url(Host, Client) -> Proto = aws_client:proto(Client), Port = aws_client:port(Client), aws_util:binary_join([Proto, <<"://">>, Host, <<":">>, Port, <<"/">>], <<"">>).