Struct aws_sdk_forecast::Client

source · [−]
pub struct Client { /* private fields */ }
Expand description

Client for Amazon Forecast Service

Client for invoking operations on Amazon Forecast Service. Each operation on Amazon Forecast Service is a method on this this struct. .send() MUST be invoked on the generated operations to dispatch the request to the service.

Examples

Constructing a client and invoking an operation

    // create a shared configuration. This can be used & shared between multiple service clients.
    let shared_config = aws_config::load_from_env().await;
    let client = aws_sdk_forecast::Client::new(&shared_config);
    // invoke an operation
    /* let rsp = client
        .<operation_name>().
        .<param>("some value")
        .send().await; */

Constructing a client with custom configuration

use aws_config::retry::RetryConfig;
let shared_config = aws_config::load_from_env().await;
let config = aws_sdk_forecast::config::Builder::from(&shared_config)
  .retry_config(RetryConfig::disabled())
  .build();
let client = aws_sdk_forecast::Client::from_conf(config);

Implementations

Creates a client with the given service configuration.

Returns the client’s configuration.

Constructs a fluent builder for the CreateAutoPredictor operation.

Constructs a fluent builder for the CreateDataset operation.

  • The fluent builder is configurable:
    • dataset_name(impl Into<String>) / set_dataset_name(Option<String>):

      A name for the dataset.

    • domain(Domain) / set_domain(Option<Domain>):

      The domain associated with the dataset. When you add a dataset to a dataset group, this value and the value specified for the Domain parameter of the CreateDatasetGroup operation must match.

      The Domain and DatasetType that you choose determine the fields that must be present in the training data that you import to the dataset. For example, if you choose the RETAIL domain and TARGET_TIME_SERIES as the DatasetType, Amazon Forecast requires item_id, timestamp, and demand fields to be present in your data. For more information, see Importing datasets.

    • dataset_type(DatasetType) / set_dataset_type(Option<DatasetType>):

      The dataset type. Valid values depend on the chosen Domain.

    • data_frequency(impl Into<String>) / set_data_frequency(Option<String>):

      The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets.

      Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, “D” indicates every day and “15min” indicates every 15 minutes.

    • schema(Schema) / set_schema(Option<Schema>):

      The schema for the dataset. The schema attributes and their order must match the fields in your data. The dataset Domain and DatasetType that you choose determine the minimum required fields in your training data. For information about the required fields for a specific dataset domain and type, see Dataset Domains and Dataset Types.

    • encryption_config(EncryptionConfig) / set_encryption_config(Option<EncryptionConfig>):

      An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

    • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

      The optional metadata that you apply to the dataset to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

      The following basic restrictions apply to tags:

      • Maximum number of tags per resource - 50.

      • For each resource, each tag key must be unique, and each tag key can have only one value.

      • Maximum key length - 128 Unicode characters in UTF-8.

      • Maximum value length - 256 Unicode characters in UTF-8.

      • If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

      • Tag keys and values are case sensitive.

      • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

  • On success, responds with CreateDatasetOutput with field(s):
  • On failure, responds with SdkError<CreateDatasetError>

Constructs a fluent builder for the CreateDatasetGroup operation.

  • The fluent builder is configurable:
    • dataset_group_name(impl Into<String>) / set_dataset_group_name(Option<String>):

      A name for the dataset group.

    • domain(Domain) / set_domain(Option<Domain>):

      The domain associated with the dataset group. When you add a dataset to a dataset group, this value and the value specified for the Domain parameter of the CreateDataset operation must match.

      The Domain and DatasetType that you choose determine the fields that must be present in training data that you import to a dataset. For example, if you choose the RETAIL domain and TARGET_TIME_SERIES as the DatasetType, Amazon Forecast requires that item_id, timestamp, and demand fields are present in your data. For more information, see Dataset groups.

    • dataset_arns(Vec<String>) / set_dataset_arns(Option<Vec<String>>):

      An array of Amazon Resource Names (ARNs) of the datasets that you want to include in the dataset group.

    • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

      The optional metadata that you apply to the dataset group to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

      The following basic restrictions apply to tags:

      • Maximum number of tags per resource - 50.

      • For each resource, each tag key must be unique, and each tag key can have only one value.

      • Maximum key length - 128 Unicode characters in UTF-8.

      • Maximum value length - 256 Unicode characters in UTF-8.

      • If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

      • Tag keys and values are case sensitive.

      • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

  • On success, responds with CreateDatasetGroupOutput with field(s):
  • On failure, responds with SdkError<CreateDatasetGroupError>

Constructs a fluent builder for the CreateDatasetImportJob operation.

  • The fluent builder is configurable:
    • dataset_import_job_name(impl Into<String>) / set_dataset_import_job_name(Option<String>):

      The name for the dataset import job. We recommend including the current timestamp in the name, for example, 20190721DatasetImport. This can help you avoid getting a ResourceAlreadyExistsException exception.

    • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):

      The Amazon Resource Name (ARN) of the Amazon Forecast dataset that you want to import data to.

    • data_source(DataSource) / set_data_source(Option<DataSource>):

      The location of the training data to import and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data. The training data must be stored in an Amazon S3 bucket.

      If encryption is used, DataSource must include an AWS Key Management Service (KMS) key and the IAM role must allow Amazon Forecast permission to access the key. The KMS key and IAM role must match those specified in the EncryptionConfig parameter of the CreateDataset operation.

    • timestamp_format(impl Into<String>) / set_timestamp_format(Option<String>):

      The format of timestamps in the dataset. The format that you specify depends on the DataFrequency specified when the dataset was created. The following formats are supported

      • “yyyy-MM-dd”

        For the following data frequencies: Y, M, W, and D

      • “yyyy-MM-dd HH:mm:ss”

        For the following data frequencies: H, 30min, 15min, and 1min; and optionally, for: Y, M, W, and D

      If the format isn’t specified, Amazon Forecast expects the format to be “yyyy-MM-dd HH:mm:ss”.

    • time_zone(impl Into<String>) / set_time_zone(Option<String>):

      A single time zone for every item in your dataset. This option is ideal for datasets with all timestamps within a single time zone, or if all timestamps are normalized to a single time zone.

      Refer to the Joda-Time API for a complete list of valid time zone names.

    • use_geolocation_for_time_zone(bool) / set_use_geolocation_for_time_zone(bool):

      Automatically derive time zone information from the geolocation attribute. This option is ideal for datasets that contain timestamps in multiple time zones and those timestamps are expressed in local time.

    • geolocation_format(impl Into<String>) / set_geolocation_format(Option<String>):

      The format of the geolocation attribute. The geolocation attribute can be formatted in one of two ways:

      • LAT_LONG - the latitude and longitude in decimal format (Example: 47.61_-122.33).

      • CC_POSTALCODE (US Only) - the country code (US), followed by the 5-digit ZIP code (Example: US_98121).

    • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

      The optional metadata that you apply to the dataset import job to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

      The following basic restrictions apply to tags:

      • Maximum number of tags per resource - 50.

      • For each resource, each tag key must be unique, and each tag key can have only one value.

      • Maximum key length - 128 Unicode characters in UTF-8.

      • Maximum value length - 256 Unicode characters in UTF-8.

      • If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

      • Tag keys and values are case sensitive.

      • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

    • format(impl Into<String>) / set_format(Option<String>):

      The format of the imported data, CSV or PARQUET. The default value is CSV.

  • On success, responds with CreateDatasetImportJobOutput with field(s):
  • On failure, responds with SdkError<CreateDatasetImportJobError>

Constructs a fluent builder for the CreateExplainability operation.

Constructs a fluent builder for the CreateExplainabilityExport operation.

Constructs a fluent builder for the CreateForecast operation.

  • The fluent builder is configurable:
    • forecast_name(impl Into<String>) / set_forecast_name(Option<String>):

      A name for the forecast.

    • predictor_arn(impl Into<String>) / set_predictor_arn(Option<String>):

      The Amazon Resource Name (ARN) of the predictor to use to generate the forecast.

    • forecast_types(Vec<String>) / set_forecast_types(Option<Vec<String>>):

      The quantiles at which probabilistic forecasts are generated. You can currently specify up to 5 quantiles per forecast. Accepted values include 0.01 to 0.99 (increments of .01 only) and mean. The mean forecast is different from the median (0.50) when the distribution is not symmetric (for example, Beta and Negative Binomial).

      The default quantiles are the quantiles you specified during predictor creation. If you didn’t specify quantiles, the default values are [“0.1”, “0.5”, “0.9”].

    • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

      The optional metadata that you apply to the forecast to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

      The following basic restrictions apply to tags:

      • Maximum number of tags per resource - 50.

      • For each resource, each tag key must be unique, and each tag key can have only one value.

      • Maximum key length - 128 Unicode characters in UTF-8.

      • Maximum value length - 256 Unicode characters in UTF-8.

      • If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

      • Tag keys and values are case sensitive.

      • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

    • time_series_selector(TimeSeriesSelector) / set_time_series_selector(Option<TimeSeriesSelector>):

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

      The TimeSeriesIdentifiers object needs the following information:

      • DataSource

      • Format

      • Schema

  • On success, responds with CreateForecastOutput with field(s):
  • On failure, responds with SdkError<CreateForecastError>

Constructs a fluent builder for the CreateForecastExportJob operation.

  • The fluent builder is configurable:
    • forecast_export_job_name(impl Into<String>) / set_forecast_export_job_name(Option<String>):

      The name for the forecast export job.

    • forecast_arn(impl Into<String>) / set_forecast_arn(Option<String>):

      The Amazon Resource Name (ARN) of the forecast that you want to export.

    • destination(DataDestination) / set_destination(Option<DataDestination>):

      The location where you want to save the forecast and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the location. The forecast must be exported to an Amazon S3 bucket.

      If encryption is used, Destination must include an AWS Key Management Service (KMS) key. The IAM role must allow Amazon Forecast permission to access the key.

    • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

      The optional metadata that you apply to the forecast export job to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

      The following basic restrictions apply to tags:

      • Maximum number of tags per resource - 50.

      • For each resource, each tag key must be unique, and each tag key can have only one value.

      • Maximum key length - 128 Unicode characters in UTF-8.

      • Maximum value length - 256 Unicode characters in UTF-8.

      • If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

      • Tag keys and values are case sensitive.

      • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

    • format(impl Into<String>) / set_format(Option<String>):

      The format of the exported data, CSV or PARQUET. The default value is CSV.

  • On success, responds with CreateForecastExportJobOutput with field(s):
  • On failure, responds with SdkError<CreateForecastExportJobError>

Constructs a fluent builder for the CreateMonitor operation.

Constructs a fluent builder for the CreatePredictor operation.

  • The fluent builder is configurable:
    • predictor_name(impl Into<String>) / set_predictor_name(Option<String>):

      A name for the predictor.

    • algorithm_arn(impl Into<String>) / set_algorithm_arn(Option<String>):

      The Amazon Resource Name (ARN) of the algorithm to use for model training. Required if PerformAutoML is not set to true.

      Supported algorithms:

      • arn:aws:forecast:::algorithm/ARIMA

      • arn:aws:forecast:::algorithm/CNN-QR

      • arn:aws:forecast:::algorithm/Deep_AR_Plus

      • arn:aws:forecast:::algorithm/ETS

      • arn:aws:forecast:::algorithm/NPTS

      • arn:aws:forecast:::algorithm/Prophet

    • forecast_horizon(i32) / set_forecast_horizon(Option<i32>):

      Specifies the number of time-steps that the model is trained to predict. The forecast horizon is also called the prediction length.

      For example, if you configure a dataset for daily data collection (using the DataFrequency parameter of the CreateDataset operation) and set the forecast horizon to 10, the model returns predictions for 10 days.

      The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.

    • forecast_types(Vec<String>) / set_forecast_types(Option<Vec<String>>):

      Specifies the forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with mean.

      The default value is [“0.10”, “0.50”, “0.9”].

    • perform_auto_ml(bool) / set_perform_auto_ml(Option<bool>):

      Whether to perform AutoML. When Amazon Forecast performs AutoML, it evaluates the algorithms it provides and chooses the best algorithm and configuration for your training dataset.

      The default value is false. In this case, you are required to specify an algorithm.

      Set PerformAutoML to true to have Amazon Forecast perform AutoML. This is a good option if you aren’t sure which algorithm is suitable for your training data. In this case, PerformHPO must be false.

    • auto_ml_override_strategy(AutoMlOverrideStrategy) / set_auto_ml_override_strategy(Option<AutoMlOverrideStrategy>):

      The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.

      Used to overide the default AutoML strategy, which is to optimize predictor accuracy. To apply an AutoML strategy that minimizes training time, use LatencyOptimized.

      This parameter is only valid for predictors trained using AutoML.

    • perform_hpo(bool) / set_perform_hpo(Option<bool>):

      Whether to perform hyperparameter optimization (HPO). HPO finds optimal hyperparameter values for your training data. The process of performing HPO is known as running a hyperparameter tuning job.

      The default value is false. In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.

      To override the default values, set PerformHPO to true and, optionally, supply the HyperParameterTuningJobConfig object. The tuning job specifies a metric to optimize, which hyperparameters participate in tuning, and the valid range for each tunable hyperparameter. In this case, you are required to specify an algorithm and PerformAutoML must be false.

      The following algorithms support HPO:

      • DeepAR+

      • CNN-QR

    • training_parameters(HashMap<String, String>) / set_training_parameters(Option<HashMap<String, String>>):

      The hyperparameters to override for model training. The hyperparameters that you can override are listed in the individual algorithms. For the list of supported algorithms, see aws-forecast-choosing-recipes.

    • evaluation_parameters(EvaluationParameters) / set_evaluation_parameters(Option<EvaluationParameters>):

      Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.

    • hpo_config(HyperParameterTuningJobConfig) / set_hpo_config(Option<HyperParameterTuningJobConfig>):

      Provides hyperparameter override values for the algorithm. If you don’t provide this parameter, Amazon Forecast uses default values. The individual algorithms specify which hyperparameters support hyperparameter optimization (HPO). For more information, see aws-forecast-choosing-recipes.

      If you included the HPOConfig object, you must set PerformHPO to true.

    • input_data_config(InputDataConfig) / set_input_data_config(Option<InputDataConfig>):

      Describes the dataset group that contains the data to use to train the predictor.

    • featurization_config(FeaturizationConfig) / set_featurization_config(Option<FeaturizationConfig>):

      The featurization configuration.

    • encryption_config(EncryptionConfig) / set_encryption_config(Option<EncryptionConfig>):

      An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

    • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

      The optional metadata that you apply to the predictor to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

      The following basic restrictions apply to tags:

      • Maximum number of tags per resource - 50.

      • For each resource, each tag key must be unique, and each tag key can have only one value.

      • Maximum key length - 128 Unicode characters in UTF-8.

      • Maximum value length - 256 Unicode characters in UTF-8.

      • If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

      • Tag keys and values are case sensitive.

      • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

    • optimization_metric(OptimizationMetric) / set_optimization_metric(Option<OptimizationMetric>):

      The accuracy metric used to optimize the predictor.

  • On success, responds with CreatePredictorOutput with field(s):
  • On failure, responds with SdkError<CreatePredictorError>

Constructs a fluent builder for the CreatePredictorBacktestExportJob operation.

Constructs a fluent builder for the CreateWhatIfAnalysis operation.

Constructs a fluent builder for the CreateWhatIfForecast operation.

Constructs a fluent builder for the CreateWhatIfForecastExport operation.

Constructs a fluent builder for the DeleteDataset operation.

Constructs a fluent builder for the DeleteDatasetGroup operation.

Constructs a fluent builder for the DeleteDatasetImportJob operation.

Constructs a fluent builder for the DeleteExplainability operation.

Constructs a fluent builder for the DeleteExplainabilityExport operation.

Constructs a fluent builder for the DeleteForecast operation.

Constructs a fluent builder for the DeleteForecastExportJob operation.

Constructs a fluent builder for the DeleteMonitor operation.

Constructs a fluent builder for the DeletePredictor operation.

Constructs a fluent builder for the DeletePredictorBacktestExportJob operation.

Constructs a fluent builder for the DeleteResourceTree operation.

Constructs a fluent builder for the DeleteWhatIfAnalysis operation.

Constructs a fluent builder for the DeleteWhatIfForecast operation.

Constructs a fluent builder for the DeleteWhatIfForecastExport operation.

Constructs a fluent builder for the DescribeAutoPredictor operation.

Constructs a fluent builder for the DescribeDataset operation.

Constructs a fluent builder for the DescribeDatasetGroup operation.

Constructs a fluent builder for the DescribeDatasetImportJob operation.

Constructs a fluent builder for the DescribeExplainability operation.

Constructs a fluent builder for the DescribeExplainabilityExport operation.

Constructs a fluent builder for the DescribeForecast operation.

Constructs a fluent builder for the DescribeForecastExportJob operation.

Constructs a fluent builder for the DescribeMonitor operation.

Constructs a fluent builder for the DescribePredictor operation.

Constructs a fluent builder for the DescribePredictorBacktestExportJob operation.

Constructs a fluent builder for the DescribeWhatIfAnalysis operation.

Constructs a fluent builder for the DescribeWhatIfForecast operation.

Constructs a fluent builder for the DescribeWhatIfForecastExport operation.

Constructs a fluent builder for the GetAccuracyMetrics operation.

Constructs a fluent builder for the ListDatasetGroups operation. This operation supports pagination; See into_paginator().

Constructs a fluent builder for the ListDatasetImportJobs operation. This operation supports pagination; See into_paginator().

  • The fluent builder is configurable:
    • next_token(impl Into<String>) / set_next_token(Option<String>):

      If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.

    • max_results(i32) / set_max_results(Option<i32>):

      The number of items to return in the response.

    • filters(Vec<Filter>) / set_filters(Option<Vec<Filter>>):

      An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the datasets that match the statement from the list, respectively. The match statement consists of a key and a value.

      Filter properties

      • Condition - The condition to apply. Valid values are IS and IS_NOT. To include the datasets that match the statement, specify IS. To exclude matching datasets, specify IS_NOT.

      • Key - The name of the parameter to filter on. Valid values are DatasetArn and Status.

      • Value - The value to match.

      For example, to list all dataset import jobs whose status is ACTIVE, you specify the following filter:

      “Filters”: [ { “Condition”: “IS”, “Key”: “Status”, “Value”: “ACTIVE” } ]

  • On success, responds with ListDatasetImportJobsOutput with field(s):
  • On failure, responds with SdkError<ListDatasetImportJobsError>

Constructs a fluent builder for the ListDatasets operation. This operation supports pagination; See into_paginator().

Constructs a fluent builder for the ListExplainabilities operation. This operation supports pagination; See into_paginator().

Constructs a fluent builder for the ListExplainabilityExports operation. This operation supports pagination; See into_paginator().

Constructs a fluent builder for the ListForecastExportJobs operation. This operation supports pagination; See into_paginator().

  • The fluent builder is configurable:
    • next_token(impl Into<String>) / set_next_token(Option<String>):

      If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.

    • max_results(i32) / set_max_results(Option<i32>):

      The number of items to return in the response.

    • filters(Vec<Filter>) / set_filters(Option<Vec<Filter>>):

      An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the forecast export jobs that match the statement from the list, respectively. The match statement consists of a key and a value.

      Filter properties

      • Condition - The condition to apply. Valid values are IS and IS_NOT. To include the forecast export jobs that match the statement, specify IS. To exclude matching forecast export jobs, specify IS_NOT.

      • Key - The name of the parameter to filter on. Valid values are ForecastArn and Status.

      • Value - The value to match.

      For example, to list all jobs that export a forecast named electricityforecast, specify the following filter:

      “Filters”: [ { “Condition”: “IS”, “Key”: “ForecastArn”, “Value”: “arn:aws:forecast:us-west-2: :forecast/electricityforecast” } ]

  • On success, responds with ListForecastExportJobsOutput with field(s):
  • On failure, responds with SdkError<ListForecastExportJobsError>

Constructs a fluent builder for the ListForecasts operation. This operation supports pagination; See into_paginator().

  • The fluent builder is configurable:
    • next_token(impl Into<String>) / set_next_token(Option<String>):

      If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.

    • max_results(i32) / set_max_results(Option<i32>):

      The number of items to return in the response.

    • filters(Vec<Filter>) / set_filters(Option<Vec<Filter>>):

      An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the forecasts that match the statement from the list, respectively. The match statement consists of a key and a value.

      Filter properties

      • Condition - The condition to apply. Valid values are IS and IS_NOT. To include the forecasts that match the statement, specify IS. To exclude matching forecasts, specify IS_NOT.

      • Key - The name of the parameter to filter on. Valid values are DatasetGroupArn, PredictorArn, and Status.

      • Value - The value to match.

      For example, to list all forecasts whose status is not ACTIVE, you would specify:

      “Filters”: [ { “Condition”: “IS_NOT”, “Key”: “Status”, “Value”: “ACTIVE” } ]

  • On success, responds with ListForecastsOutput with field(s):
  • On failure, responds with SdkError<ListForecastsError>

Constructs a fluent builder for the ListMonitorEvaluations operation. This operation supports pagination; See into_paginator().

Constructs a fluent builder for the ListMonitors operation. This operation supports pagination; See into_paginator().

  • The fluent builder is configurable:
    • next_token(impl Into<String>) / set_next_token(Option<String>):

      If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.

    • max_results(i32) / set_max_results(Option<i32>):

      The maximum number of monitors to include in the response.

    • filters(Vec<Filter>) / set_filters(Option<Vec<Filter>>):

      An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the resources that match the statement from the list. The match statement consists of a key and a value.

      Filter properties

      • Condition - The condition to apply. Valid values are IS and IS_NOT.

      • Key - The name of the parameter to filter on. The only valid value is Status.

      • Value - The value to match.

      For example, to list all monitors who’s status is ACTIVE, you would specify:

      “Filters”: [ { “Condition”: “IS”, “Key”: “Status”, “Value”: “ACTIVE” } ]

  • On success, responds with ListMonitorsOutput with field(s):
  • On failure, responds with SdkError<ListMonitorsError>

Constructs a fluent builder for the ListPredictorBacktestExportJobs operation. This operation supports pagination; See into_paginator().

Constructs a fluent builder for the ListPredictors operation. This operation supports pagination; See into_paginator().

  • The fluent builder is configurable:
    • next_token(impl Into<String>) / set_next_token(Option<String>):

      If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.

    • max_results(i32) / set_max_results(Option<i32>):

      The number of items to return in the response.

    • filters(Vec<Filter>) / set_filters(Option<Vec<Filter>>):

      An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the predictors that match the statement from the list, respectively. The match statement consists of a key and a value.

      Filter properties

      • Condition - The condition to apply. Valid values are IS and IS_NOT. To include the predictors that match the statement, specify IS. To exclude matching predictors, specify IS_NOT.

      • Key - The name of the parameter to filter on. Valid values are DatasetGroupArn and Status.

      • Value - The value to match.

      For example, to list all predictors whose status is ACTIVE, you would specify:

      “Filters”: [ { “Condition”: “IS”, “Key”: “Status”, “Value”: “ACTIVE” } ]

  • On success, responds with ListPredictorsOutput with field(s):
  • On failure, responds with SdkError<ListPredictorsError>

Constructs a fluent builder for the ListTagsForResource operation.

Constructs a fluent builder for the ListWhatIfAnalyses operation. This operation supports pagination; See into_paginator().

  • The fluent builder is configurable:
    • next_token(impl Into<String>) / set_next_token(Option<String>):

      If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.

    • max_results(i32) / set_max_results(Option<i32>):

      The number of items to return in the response.

    • filters(Vec<Filter>) / set_filters(Option<Vec<Filter>>):

      An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the what-if analysis jobs that match the statement from the list, respectively. The match statement consists of a key and a value.

      Filter properties

      • Condition - The condition to apply. Valid values are IS and IS_NOT. To include the what-if analysis jobs that match the statement, specify IS. To exclude matching what-if analysis jobs, specify IS_NOT.

      • Key - The name of the parameter to filter on. Valid values are WhatIfAnalysisArn and Status.

      • Value - The value to match.

      For example, to list all jobs that export a forecast named electricityWhatIf, specify the following filter:

      “Filters”: [ { “Condition”: “IS”, “Key”: “WhatIfAnalysisArn”, “Value”: “arn:aws:forecast:us-west-2: :forecast/electricityWhatIf” } ]

  • On success, responds with ListWhatIfAnalysesOutput with field(s):
  • On failure, responds with SdkError<ListWhatIfAnalysesError>

Constructs a fluent builder for the ListWhatIfForecastExports operation. This operation supports pagination; See into_paginator().

  • The fluent builder is configurable:
    • next_token(impl Into<String>) / set_next_token(Option<String>):

      If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next
 request. Tokens expire after 24 hours.

    • max_results(i32) / set_max_results(Option<i32>):

      The number of items to return in the response.

    • filters(Vec<Filter>) / set_filters(Option<Vec<Filter>>):

      An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the what-if forecast export jobs that match the statement from the list, respectively. The match statement consists of a key and a value.

      Filter properties

      • Condition - The condition to apply. Valid values are IS and IS_NOT. To include the forecast export jobs that match the statement, specify IS. To exclude matching forecast export jobs, specify IS_NOT.

      • Key - The name of the parameter to filter on. Valid values are WhatIfForecastExportArn and Status.

      • Value - The value to match.

      For example, to list all jobs that export a forecast named electricityWIFExport, specify the following filter:

      “Filters”: [ { “Condition”: “IS”, “Key”: “WhatIfForecastExportArn”, “Value”: “arn:aws:forecast:us-west-2: :forecast/electricityWIFExport” } ]

  • On success, responds with ListWhatIfForecastExportsOutput with field(s):
  • On failure, responds with SdkError<ListWhatIfForecastExportsError>

Constructs a fluent builder for the ListWhatIfForecasts operation. This operation supports pagination; See into_paginator().

  • The fluent builder is configurable:
    • next_token(impl Into<String>) / set_next_token(Option<String>):

      If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next
 request. Tokens expire after 24 hours.

    • max_results(i32) / set_max_results(Option<i32>):

      The number of items to return in the response.

    • filters(Vec<Filter>) / set_filters(Option<Vec<Filter>>):

      An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the what-if forecast export jobs that match the statement from the list, respectively. The match statement consists of a key and a value.

      Filter properties

      • Condition - The condition to apply. Valid values are IS and IS_NOT. To include the forecast export jobs that match the statement, specify IS. To exclude matching forecast export jobs, specify IS_NOT.

      • Key - The name of the parameter to filter on. Valid values are WhatIfForecastArn and Status.

      • Value - The value to match.

      For example, to list all jobs that export a forecast named electricityWhatIfForecast, specify the following filter:

      “Filters”: [ { “Condition”: “IS”, “Key”: “WhatIfForecastArn”, “Value”: “arn:aws:forecast:us-west-2: :forecast/electricityWhatIfForecast” } ]

  • On success, responds with ListWhatIfForecastsOutput with field(s):
  • On failure, responds with SdkError<ListWhatIfForecastsError>

Constructs a fluent builder for the ResumeResource operation.

Constructs a fluent builder for the StopResource operation.

Constructs a fluent builder for the TagResource operation.

  • The fluent builder is configurable:
    • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):

      The Amazon Resource Name (ARN) that identifies the resource for which to list the tags.

    • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

      The tags to add to the resource. A tag is an array of key-value pairs.

      The following basic restrictions apply to tags:

      • Maximum number of tags per resource - 50.

      • For each resource, each tag key must be unique, and each tag key can have only one value.

      • Maximum key length - 128 Unicode characters in UTF-8.

      • Maximum value length - 256 Unicode characters in UTF-8.

      • If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

      • Tag keys and values are case sensitive.

      • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

  • On success, responds with TagResourceOutput
  • On failure, responds with SdkError<TagResourceError>

Constructs a fluent builder for the UntagResource operation.

Constructs a fluent builder for the UpdateDatasetGroup operation.

Creates a client with the given service config and connector override.

Creates a new client from a shared config.

Creates a new client from the service Config.

Trait Implementations

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