Struct aws_sdk_forecast::Client

pub struct Client { /* private fields */ }

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.

Constructing a Client

A Config is required to construct a client. For most use cases, the aws-config crate should be used to automatically resolve this config using aws_config::load_from_env(), since this will resolve an SdkConfig which can be shared across multiple different AWS SDK clients. This config resolution process can be customized by calling aws_config::from_env() instead, which returns a ConfigLoader that uses the builder pattern to customize the default config.

In the simplest case, creating a client looks as follows:

let config = aws_config::load_from_env().await;
let client = aws_sdk_forecast::Client::new(&config);

Occasionally, SDKs may have additional service-specific that can be set on the Config that is absent from SdkConfig, or slightly different settings for a specific client may be desired. The Config struct implements From<&SdkConfig>, so setting these specific settings can be done as follows:

let sdk_config = ::aws_config::load_from_env().await;
let config = aws_sdk_forecast::config::Builder::from(&sdk_config)
    .some_service_specific_setting("value")
    .build();

See the aws-config docs and Config for more information on customizing configuration.

Note: Client construction is expensive due to connection thread pool initialization, and should be done once at application start-up.

Using the Client

A client has a function for every operation that can be performed by the service. For example, the CreateAutoPredictor operation has a Client::create_auto_predictor, function which returns a builder for that operation. The fluent builder ultimately has a send() function that returns an async future that returns a result, as illustrated below:

let result = client.create_auto_predictor()
    .predictor_name("example")
    .send()
    .await;

The underlying HTTP requests that get made by this can be modified with the customize_operation function on the fluent builder. See the customize module for more information.

Implementations

impl Client

pub fn create_auto_predictor(&self) -> CreateAutoPredictorFluentBuilder

Constructs a fluent builder for the CreateAutoPredictor operation.

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

    A unique name for the predictor

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

    The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.

    The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the TARGET_TIME_SERIES dataset length. If you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.

    If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the forecast horizon parameter. You can meet this requirement by providing longer time-series in the dataset.

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

    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.

  • forecast_dimensions(impl Into<String>) / set_forecast_dimensions(Option<Vec<String>>):

    An array of dimension (field) names that specify how to group the generated forecast.

    For example, if you are generating forecasts for item sales across all your stores, and your dataset contains a store_id field, you would specify store_id as a dimension to group sales forecasts for each store.

  • forecast_frequency(impl Into<String>) / set_forecast_frequency(Option<String>):

    The frequency of predictions in a forecast.

    Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, “1D” indicates every day and “15min” indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:

    • Minute - 1-59

    • Hour - 1-23

    • Day - 1-6

    • Week - 1-4

    • Month - 1-11

    • Year - 1

    Thus, if you want every other week forecasts, specify “2W”. Or, if you want quarterly forecasts, you specify “3M”.

    The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.

    When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the RELATED_TIME_SERIES dataset frequency.

  • data_config(DataConfig) / set_data_config(Option<DataConfig>):

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

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

    An Key Management Service (KMS) key and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key. You can specify this optional object in the CreateDataset and CreatePredictor requests.

  • reference_predictor_arn(impl Into<String>) / set_reference_predictor_arn(Option<String>):

    The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a predictor. When creating a new predictor, do not specify a value for this parameter.

    When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and PredictorName. The value for PredictorName must be a unique predictor name.

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

    The accuracy metric used to optimize the predictor.

  • explain_predictor(bool) / set_explain_predictor(Option<bool>):

    Create an Explainability resource for the predictor.

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

    Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.

    The following restrictions apply to tags:

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

    • Maximum number of tags per resource: 50.

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

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

    • Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.

    • Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws as its prefix but the key does not, 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. You cannot edit or delete tag keys with this prefix.

  • monitor_config(MonitorConfig) / set_monitor_config(Option<MonitorConfig>):

    The configuration details for predictor monitoring. Provide a name for the monitor resource to enable predictor monitoring.

    Predictor monitoring allows you to see how your predictor’s performance changes over time. For more information, see Predictor Monitoring.

  • time_alignment_boundary(TimeAlignmentBoundary) / set_time_alignment_boundary(Option<TimeAlignmentBoundary>):

    The time boundary Forecast uses to align and aggregate any data that doesn’t align with your forecast frequency. Provide the unit of time and the time boundary as a key value pair. For more information on specifying a time boundary, see Specifying a Time Boundary. If you don’t provide a time boundary, Forecast uses a set of Default Time Boundaries.

• On success, responds with CreateAutoPredictorOutput with field(s):
  • predictor_arn(Option<String>):

    The Amazon Resource Name (ARN) of the predictor.

• On failure, responds with SdkError<CreateAutoPredictorError>

impl Client

pub fn create_dataset(&self) -> CreateDatasetFluentBuilder

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 an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, “1D” indicates every day and “15min” indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:

    • Minute - 1-59

    • Hour - 1-23

    • Day - 1-6

    • Week - 1-4

    • Month - 1-11

    • Year - 1

    Thus, if you want every other week forecasts, specify “2W”. Or, if you want quarterly forecasts, you specify “3M”.

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

  • tags(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 Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

• On success, responds with CreateDatasetOutput with field(s):
  • dataset_arn(Option<String>):

    The Amazon Resource Name (ARN) of the dataset.

• On failure, responds with SdkError<CreateDatasetError>

impl Client

pub fn create_dataset_group(&self) -> CreateDatasetGroupFluentBuilder

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(impl Into<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(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 Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

• On success, responds with CreateDatasetGroupOutput with field(s):
  • dataset_group_arn(Option<String>):

    The Amazon Resource Name (ARN) of the dataset group.

• On failure, responds with SdkError<CreateDatasetGroupError>

impl Client

pub fn create_dataset_import_job(&self) -> CreateDatasetImportJobFluentBuilder

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 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 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(Option<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(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 Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

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

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

  • import_mode(ImportMode) / set_import_mode(Option<ImportMode>):

    Specifies whether the dataset import job is a FULL or INCREMENTAL import. A FULL dataset import replaces all of the existing data with the newly imported data. An INCREMENTAL import appends the imported data to the existing data.

• On success, responds with CreateDatasetImportJobOutput with field(s):
  • dataset_import_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the dataset import job.

• On failure, responds with SdkError<CreateDatasetImportJobError>

impl Client

pub fn create_explainability(&self) -> CreateExplainabilityFluentBuilder

Constructs a fluent builder for the CreateExplainability operation.

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

    A unique name for the Explainability.

  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability.

  • explainability_config(ExplainabilityConfig) / set_explainability_config(Option<ExplainabilityConfig>):

    The configuration settings that define the granularity of time series and time points for the Explainability.

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

    The source of your data, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the data and, optionally, an Key Management Service (KMS) key.

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

    Defines the fields of a dataset.

  • enable_visualization(bool) / set_enable_visualization(Option<bool>):

    Create an Explainability visualization that is viewable within the Amazon Web Services console.

  • start_date_time(impl Into<String>) / set_start_date_time(Option<String>):

    If TimePointGranularity is set to SPECIFIC, define the first point for the Explainability.

    Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)

  • end_date_time(impl Into<String>) / set_end_date_time(Option<String>):

    If TimePointGranularity is set to SPECIFIC, define the last time point for the Explainability.

    Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)

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

    Optional metadata to help you categorize and organize your resources. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.

    The following restrictions apply to tags:

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

    • Maximum number of tags per resource: 50.

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

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

    • Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.

    • Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws as its prefix but the key does not, 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. You cannot edit or delete tag keys with this prefix.

• On success, responds with CreateExplainabilityOutput with field(s):
  • explainability_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Explainability.

• On failure, responds with SdkError<CreateExplainabilityError>

impl Client

pub fn create_explainability_export( &self ) -> CreateExplainabilityExportFluentBuilder

Constructs a fluent builder for the CreateExplainabilityExport operation.

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

    A unique name for the Explainability export.

  • explainability_arn(impl Into<String>) / set_explainability_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Explainability to export.

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

    The destination for an export job. Provide an S3 path, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an Key Management Service (KMS) key (optional).

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

    Optional metadata to help you categorize and organize your resources. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.

    The following restrictions apply to tags:

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

    • Maximum number of tags per resource: 50.

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

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

    • Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.

    • Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws as its prefix but the key does not, 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. You cannot edit or delete tag keys with this prefix.

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

    The format of the exported data, CSV or PARQUET.

• On success, responds with CreateExplainabilityExportOutput with field(s):
  • explainability_export_arn(Option<String>):

    The Amazon Resource Name (ARN) of the export.

• On failure, responds with SdkError<CreateExplainabilityExportError>

impl Client

pub fn create_forecast(&self) -> CreateForecastFluentBuilder

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(impl Into<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(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 Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

  • 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):
  • forecast_arn(Option<String>):

    The Amazon Resource Name (ARN) of the forecast.

• On failure, responds with SdkError<CreateForecastError>

impl Client

pub fn create_forecast_export_job(&self) -> CreateForecastExportJobFluentBuilder

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 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 Key Management Service (KMS) key. The IAM role must allow Amazon Forecast permission to access the key.

  • tags(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 Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

  • 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):
  • forecast_export_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the export job.

• On failure, responds with SdkError<CreateForecastExportJobError>

impl Client

pub fn create_monitor(&self) -> CreateMonitorFluentBuilder

Constructs a fluent builder for the CreateMonitor operation.

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

    The name of the monitor resource.

  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):

    The Amazon Resource Name (ARN) of the predictor to monitor.

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

    A list of tags to apply to the monitor resource.

• On success, responds with CreateMonitorOutput with field(s):
  • monitor_arn(Option<String>):

    The Amazon Resource Name (ARN) of the monitor resource.

• On failure, responds with SdkError<CreateMonitorError>

impl Client

pub fn create_predictor(&self) -> CreatePredictorFluentBuilder

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(impl Into<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 Amazon Web Services 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(impl Into<String>, impl Into<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 Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

  • tags(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 Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

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

    The accuracy metric used to optimize the predictor.

• On success, responds with CreatePredictorOutput with field(s):
  • predictor_arn(Option<String>):

    The Amazon Resource Name (ARN) of the predictor.

• On failure, responds with SdkError<CreatePredictorError>

impl Client

pub fn create_predictor_backtest_export_job( &self ) -> CreatePredictorBacktestExportJobFluentBuilder

Constructs a fluent builder for the CreatePredictorBacktestExportJob operation.

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

    The name for the backtest export job.

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

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

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

    The destination for an export job. Provide an S3 path, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an Key Management Service (KMS) key (optional).

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

    Optional metadata to help you categorize and organize your backtests. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.

    The following restrictions apply to tags:

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

    • Maximum number of tags per resource: 50.

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

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

    • Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.

    • Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws as its prefix but the key does not, 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. You cannot edit or delete tag keys with this prefix.

  • 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 CreatePredictorBacktestExportJobOutput with field(s):
  • predictor_backtest_export_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the predictor backtest export job that you want to export.

• On failure, responds with SdkError<CreatePredictorBacktestExportJobError>

impl Client

pub fn create_what_if_analysis(&self) -> CreateWhatIfAnalysisFluentBuilder

Constructs a fluent builder for the CreateWhatIfAnalysis operation.

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

    The name of the what-if analysis. Each name must be unique.

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

    The Amazon Resource Name (ARN) of the baseline forecast.

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

    Defines the set of time series that are used in the what-if analysis with a TimeSeriesIdentifiers object. What-if analyses are performed only for the time series in this object.

    The TimeSeriesIdentifiers object needs the following information:

    • DataSource

    • Format

    • Schema

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

    A list of tags to apply to the what if forecast.

• On success, responds with CreateWhatIfAnalysisOutput with field(s):
  • what_if_analysis_arn(Option<String>):

    The Amazon Resource Name (ARN) of the what-if analysis.

• On failure, responds with SdkError<CreateWhatIfAnalysisError>

impl Client

pub fn create_what_if_forecast(&self) -> CreateWhatIfForecastFluentBuilder

Constructs a fluent builder for the CreateWhatIfForecast operation.

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

    The name of the what-if forecast. Names must be unique within each what-if analysis.

  • what_if_analysis_arn(impl Into<String>) / set_what_if_analysis_arn(Option<String>):

    The Amazon Resource Name (ARN) of the what-if analysis.

  • time_series_transformations(TimeSeriesTransformation) / set_time_series_transformations(Option<Vec<TimeSeriesTransformation>>):

    The transformations that are applied to the baseline time series. Each transformation contains an action and a set of conditions. An action is applied only when all conditions are met. If no conditions are provided, the action is applied to all items.

  • time_series_replacements_data_source(TimeSeriesReplacementsDataSource) / set_time_series_replacements_data_source(Option<TimeSeriesReplacementsDataSource>):

    The replacement time series dataset, which contains the rows that you want to change in the related time series dataset. A replacement time series does not need to contain all rows that are in the baseline related time series. Include only the rows (measure-dimension combinations) that you want to include in the what-if forecast.

    This dataset is merged with the original time series to create a transformed dataset that is used for the what-if analysis.

    This dataset should contain the items to modify (such as item_id or workforce_type), any relevant dimensions, the timestamp column, and at least one of the related time series columns. This file should not contain duplicate timestamps for the same time series.

    Timestamps and item_ids not included in this dataset are not included in the what-if analysis.

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

    A list of tags to apply to the what if forecast.

• On success, responds with CreateWhatIfForecastOutput with field(s):
  • what_if_forecast_arn(Option<String>):

    The Amazon Resource Name (ARN) of the what-if forecast.

• On failure, responds with SdkError<CreateWhatIfForecastError>

impl Client

pub fn create_what_if_forecast_export( &self ) -> CreateWhatIfForecastExportFluentBuilder

Constructs a fluent builder for the CreateWhatIfForecastExport operation.

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

    The name of the what-if forecast to export.

  • what_if_forecast_arns(impl Into<String>) / set_what_if_forecast_arns(Option<Vec<String>>):

    The list of what-if forecast Amazon Resource Names (ARNs) to export.

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

    The location where you want to save the forecast and an 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 Key Management Service (KMS) key. The IAM role must allow Amazon Forecast permission to access the key.

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

    A list of tags to apply to the what if forecast.

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

    The format of the exported data, CSV or PARQUET.

• On success, responds with CreateWhatIfForecastExportOutput with field(s):
  • what_if_forecast_export_arn(Option<String>):

    The Amazon Resource Name (ARN) of the what-if forecast.

• On failure, responds with SdkError<CreateWhatIfForecastExportError>

impl Client

pub fn delete_dataset(&self) -> DeleteDatasetFluentBuilder

Constructs a fluent builder for the DeleteDataset operation.

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

    The Amazon Resource Name (ARN) of the dataset to delete.

• On success, responds with DeleteDatasetOutput
• On failure, responds with SdkError<DeleteDatasetError>

impl Client

pub fn delete_dataset_group(&self) -> DeleteDatasetGroupFluentBuilder

Constructs a fluent builder for the DeleteDatasetGroup operation.

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

    The Amazon Resource Name (ARN) of the dataset group to delete.

• On success, responds with DeleteDatasetGroupOutput
• On failure, responds with SdkError<DeleteDatasetGroupError>

impl Client

pub fn delete_dataset_import_job(&self) -> DeleteDatasetImportJobFluentBuilder

Constructs a fluent builder for the DeleteDatasetImportJob operation.

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

    The Amazon Resource Name (ARN) of the dataset import job to delete.

• On success, responds with DeleteDatasetImportJobOutput
• On failure, responds with SdkError<DeleteDatasetImportJobError>

impl Client

pub fn delete_explainability(&self) -> DeleteExplainabilityFluentBuilder

Constructs a fluent builder for the DeleteExplainability operation.

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

    The Amazon Resource Name (ARN) of the Explainability resource to delete.

• On success, responds with DeleteExplainabilityOutput
• On failure, responds with SdkError<DeleteExplainabilityError>

impl Client

pub fn delete_explainability_export( &self ) -> DeleteExplainabilityExportFluentBuilder

Constructs a fluent builder for the DeleteExplainabilityExport operation.

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

    The Amazon Resource Name (ARN) of the Explainability export to delete.

• On success, responds with DeleteExplainabilityExportOutput
• On failure, responds with SdkError<DeleteExplainabilityExportError>

impl Client

pub fn delete_forecast(&self) -> DeleteForecastFluentBuilder

Constructs a fluent builder for the DeleteForecast operation.

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

    The Amazon Resource Name (ARN) of the forecast to delete.

• On success, responds with DeleteForecastOutput
• On failure, responds with SdkError<DeleteForecastError>

impl Client

pub fn delete_forecast_export_job(&self) -> DeleteForecastExportJobFluentBuilder

Constructs a fluent builder for the DeleteForecastExportJob operation.

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

    The Amazon Resource Name (ARN) of the forecast export job to delete.

• On success, responds with DeleteForecastExportJobOutput
• On failure, responds with SdkError<DeleteForecastExportJobError>

impl Client

pub fn delete_monitor(&self) -> DeleteMonitorFluentBuilder

Constructs a fluent builder for the DeleteMonitor operation.

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

    The Amazon Resource Name (ARN) of the monitor resource to delete.

• On success, responds with DeleteMonitorOutput
• On failure, responds with SdkError<DeleteMonitorError>

impl Client

pub fn delete_predictor(&self) -> DeletePredictorFluentBuilder

Constructs a fluent builder for the DeletePredictor operation.

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

    The Amazon Resource Name (ARN) of the predictor to delete.

• On success, responds with DeletePredictorOutput
• On failure, responds with SdkError<DeletePredictorError>

impl Client

pub fn delete_predictor_backtest_export_job( &self ) -> DeletePredictorBacktestExportJobFluentBuilder

Constructs a fluent builder for the DeletePredictorBacktestExportJob operation.

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

    The Amazon Resource Name (ARN) of the predictor backtest export job to delete.

• On success, responds with DeletePredictorBacktestExportJobOutput
• On failure, responds with SdkError<DeletePredictorBacktestExportJobError>

impl Client

pub fn delete_resource_tree(&self) -> DeleteResourceTreeFluentBuilder

Constructs a fluent builder for the DeleteResourceTree operation.

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

    The Amazon Resource Name (ARN) of the parent resource to delete. All child resources of the parent resource will also be deleted.

• On success, responds with DeleteResourceTreeOutput
• On failure, responds with SdkError<DeleteResourceTreeError>

impl Client

pub fn delete_what_if_analysis(&self) -> DeleteWhatIfAnalysisFluentBuilder

Constructs a fluent builder for the DeleteWhatIfAnalysis operation.

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

    The Amazon Resource Name (ARN) of the what-if analysis that you want to delete.

• On success, responds with DeleteWhatIfAnalysisOutput
• On failure, responds with SdkError<DeleteWhatIfAnalysisError>

impl Client

pub fn delete_what_if_forecast(&self) -> DeleteWhatIfForecastFluentBuilder

Constructs a fluent builder for the DeleteWhatIfForecast operation.

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

    The Amazon Resource Name (ARN) of the what-if forecast that you want to delete.

• On success, responds with DeleteWhatIfForecastOutput
• On failure, responds with SdkError<DeleteWhatIfForecastError>

impl Client

pub fn delete_what_if_forecast_export( &self ) -> DeleteWhatIfForecastExportFluentBuilder

Constructs a fluent builder for the DeleteWhatIfForecastExport operation.

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

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

• On success, responds with DeleteWhatIfForecastExportOutput
• On failure, responds with SdkError<DeleteWhatIfForecastExportError>

impl Client

pub fn describe_auto_predictor(&self) -> DescribeAutoPredictorFluentBuilder

Constructs a fluent builder for the DescribeAutoPredictor operation.

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

    The Amazon Resource Name (ARN) of the predictor.

• On success, responds with DescribeAutoPredictorOutput with field(s):
  • predictor_arn(Option<String>):

    The Amazon Resource Name (ARN) of the predictor

  • predictor_name(Option<String>):

    The name of the predictor.

  • forecast_horizon(Option<i32>):

    The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.

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

    The forecast types used during predictor training. Default value is [“0.1”,“0.5”,“0.9”].

  • forecast_frequency(Option<String>):

    The frequency of predictions in a forecast.

    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, “Y” indicates every year and “5min” indicates every five minutes.

  • forecast_dimensions(Option<Vec<String>>):

    An array of dimension (field) names that specify the attributes used to group your time series.

  • dataset_import_job_arns(Option<Vec<String>>):

    An array of the ARNs of the dataset import jobs used to import training data for the predictor.

  • data_config(Option<DataConfig>):

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

  • encryption_config(Option<EncryptionConfig>):

    An Key Management Service (KMS) key and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key. You can specify this optional object in the CreateDataset and CreatePredictor requests.

  • reference_predictor_summary(Option<ReferencePredictorSummary>):

    The ARN and state of the reference predictor. This parameter is only valid for retrained or upgraded predictors.

  • estimated_time_remaining_in_minutes(Option<i64>):

    The estimated time remaining in minutes for the predictor training job to complete.

  • status(Option<String>):

    The status of the predictor. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

  • message(Option<String>):

    In the event of an error, a message detailing the cause of the error.

  • creation_time(Option<DateTime>):

    The timestamp of the CreateAutoPredictor request.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

  • optimization_metric(Option<OptimizationMetric>):

    The accuracy metric used to optimize the predictor.

  • explainability_info(Option<ExplainabilityInfo>):

    Provides the status and ARN of the Predictor Explainability.

  • monitor_info(Option<MonitorInfo>):

    A object with the Amazon Resource Name (ARN) and status of the monitor resource.

  • time_alignment_boundary(Option<TimeAlignmentBoundary>):

    The time boundary Forecast uses when aggregating data.

• On failure, responds with SdkError<DescribeAutoPredictorError>

impl Client

pub fn describe_dataset(&self) -> DescribeDatasetFluentBuilder

Constructs a fluent builder for the DescribeDataset operation.

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

    The Amazon Resource Name (ARN) of the dataset.

• On success, responds with DescribeDatasetOutput with field(s):
  • dataset_arn(Option<String>):

    The Amazon Resource Name (ARN) of the dataset.

  • dataset_name(Option<String>):

    The name of the dataset.

  • domain(Option<Domain>):

    The domain associated with the dataset.

  • dataset_type(Option<DatasetType>):

    The dataset type.

  • data_frequency(Option<String>):

    The frequency of data collection.

    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, “M” indicates every month and “30min” indicates every 30 minutes.

  • schema(Option<Schema>):

    An array of SchemaAttribute objects that specify the dataset fields. Each SchemaAttribute specifies the name and data type of a field.

  • encryption_config(Option<EncryptionConfig>):

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

  • status(Option<String>):

    The status of the dataset. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

    • UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED

    The UPDATE states apply while data is imported to the dataset from a call to the CreateDatasetImportJob operation and reflect the status of the dataset import job. For example, when the import job status is CREATE_IN_PROGRESS, the status of the dataset is UPDATE_IN_PROGRESS.

    The Status of the dataset must be ACTIVE before you can import training data.

  • creation_time(Option<DateTime>):

    When the dataset was created.

  • last_modification_time(Option<DateTime>):

    When you create a dataset, LastModificationTime is the same as CreationTime. While data is being imported to the dataset, LastModificationTime is the current time of the DescribeDataset call. After a CreateDatasetImportJob operation has finished, LastModificationTime is when the import job completed or failed.

• On failure, responds with SdkError<DescribeDatasetError>

impl Client

pub fn describe_dataset_group(&self) -> DescribeDatasetGroupFluentBuilder

Constructs a fluent builder for the DescribeDatasetGroup operation.

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

    The Amazon Resource Name (ARN) of the dataset group.

• On success, responds with DescribeDatasetGroupOutput with field(s):
  • dataset_group_name(Option<String>):

    The name of the dataset group.

  • dataset_group_arn(Option<String>):

    The ARN of the dataset group.

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

    An array of Amazon Resource Names (ARNs) of the datasets contained in the dataset group.

  • domain(Option<Domain>):

    The domain associated with the dataset group.

  • status(Option<String>):

    The status of the dataset group. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

    • UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED

    The UPDATE states apply when you call the UpdateDatasetGroup operation.

    The Status of the dataset group must be ACTIVE before you can use the dataset group to create a predictor.

  • creation_time(Option<DateTime>):

    When the dataset group was created.

  • last_modification_time(Option<DateTime>):

    When the dataset group was created or last updated from a call to the UpdateDatasetGroup operation. While the dataset group is being updated, LastModificationTime is the current time of the DescribeDatasetGroup call.

• On failure, responds with SdkError<DescribeDatasetGroupError>

impl Client

pub fn describe_dataset_import_job( &self ) -> DescribeDatasetImportJobFluentBuilder

Constructs a fluent builder for the DescribeDatasetImportJob operation.

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

    The Amazon Resource Name (ARN) of the dataset import job.

• On success, responds with DescribeDatasetImportJobOutput with field(s):
  • dataset_import_job_name(Option<String>):

    The name of the dataset import job.

  • dataset_import_job_arn(Option<String>):

    The ARN of the dataset import job.

  • dataset_arn(Option<String>):

    The Amazon Resource Name (ARN) of the dataset that the training data was imported to.

  • 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

  • time_zone(Option<String>):

    The single time zone applied to every item in the dataset

  • use_geolocation_for_time_zone(bool):

    Whether TimeZone is automatically derived from the geolocation attribute.

  • geolocation_format(Option<String>):

    The format of the geolocation attribute. Valid Values:“LAT_LONG” and “CC_POSTALCODE”.

  • data_source(Option<DataSource>):

    The location of the training data to import and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data.

    If encryption is used, DataSource includes an Key Management Service (KMS) key.

  • estimated_time_remaining_in_minutes(Option<i64>):

    The estimated time remaining in minutes for the dataset import job to complete.

  • field_statistics(Option<HashMap<String, Statistics>>):

    Statistical information about each field in the input data.

  • data_size(Option<f64>):

    The size of the dataset in gigabytes (GB) after the import job has finished.

  • status(Option<String>):

    The status of the dataset import job. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

  • message(Option<String>):

    If an error occurred, an informational message about the error.

  • creation_time(Option<DateTime>):

    When the dataset import job was created.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

  • format(Option<String>):

    The format of the imported data, CSV or PARQUET.

  • import_mode(Option<ImportMode>):

    The import mode of the dataset import job, FULL or INCREMENTAL.

• On failure, responds with SdkError<DescribeDatasetImportJobError>

impl Client

pub fn describe_explainability(&self) -> DescribeExplainabilityFluentBuilder

Constructs a fluent builder for the DescribeExplainability operation.

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

    The Amazon Resource Name (ARN) of the Explaianability to describe.

• On success, responds with DescribeExplainabilityOutput with field(s):
  • explainability_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Explainability.

  • explainability_name(Option<String>):

    The name of the Explainability.

  • resource_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability resource.

  • explainability_config(Option<ExplainabilityConfig>):

    The configuration settings that define the granularity of time series and time points for the Explainability.

  • enable_visualization(Option<bool>):

    Whether the visualization was enabled for the Explainability resource.

  • data_source(Option<DataSource>):

    The source of your data, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the data and, optionally, an Key Management Service (KMS) key.

  • schema(Option<Schema>):

    Defines the fields of a dataset.

  • start_date_time(Option<String>):

    If TimePointGranularity is set to SPECIFIC, the first time point in the Explainability.

  • end_date_time(Option<String>):

    If TimePointGranularity is set to SPECIFIC, the last time point in the Explainability.

  • estimated_time_remaining_in_minutes(Option<i64>):

    The estimated time remaining in minutes for the CreateExplainability job to complete.

  • message(Option<String>):

    If an error occurred, a message about the error.

  • status(Option<String>):

    The status of the Explainability resource. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

  • creation_time(Option<DateTime>):

    When the Explainability resource was created.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

• On failure, responds with SdkError<DescribeExplainabilityError>

impl Client

pub fn describe_explainability_export( &self ) -> DescribeExplainabilityExportFluentBuilder

Constructs a fluent builder for the DescribeExplainabilityExport operation.

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

    The Amazon Resource Name (ARN) of the Explainability export.

• On success, responds with DescribeExplainabilityExportOutput with field(s):
  • explainability_export_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Explainability export.

  • explainability_export_name(Option<String>):

    The name of the Explainability export.

  • explainability_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Explainability export.

  • destination(Option<DataDestination>):

    The destination for an export job. Provide an S3 path, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an Key Management Service (KMS) key (optional).

  • message(Option<String>):

    Information about any errors that occurred during the export.

  • status(Option<String>):

    The status of the Explainability export. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

  • creation_time(Option<DateTime>):

    When the Explainability export was created.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

  • format(Option<String>):

    The format of the exported data, CSV or PARQUET.

• On failure, responds with SdkError<DescribeExplainabilityExportError>

impl Client

pub fn describe_forecast(&self) -> DescribeForecastFluentBuilder

Constructs a fluent builder for the DescribeForecast operation.

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

    The Amazon Resource Name (ARN) of the forecast.

• On success, responds with DescribeForecastOutput with field(s):
  • forecast_arn(Option<String>):

    The forecast ARN as specified in the request.

  • forecast_name(Option<String>):

    The name of the forecast.

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

    The quantiles at which probabilistic forecasts were generated.

  • predictor_arn(Option<String>):

    The ARN of the predictor used to generate the forecast.

  • dataset_group_arn(Option<String>):

    The ARN of the dataset group that provided the data used to train the predictor.

  • estimated_time_remaining_in_minutes(Option<i64>):

    The estimated time remaining in minutes for the forecast job to complete.

  • status(Option<String>):

    The status of the forecast. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

    The Status of the forecast must be ACTIVE before you can query or export the forecast.

  • message(Option<String>):

    If an error occurred, an informational message about the error.

  • creation_time(Option<DateTime>):

    When the forecast creation task was created.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

  • time_series_selector(Option<TimeSeriesSelector>):

    The time series to include in the forecast.

• On failure, responds with SdkError<DescribeForecastError>

impl Client

pub fn describe_forecast_export_job( &self ) -> DescribeForecastExportJobFluentBuilder

Constructs a fluent builder for the DescribeForecastExportJob operation.

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

    The Amazon Resource Name (ARN) of the forecast export job.

• On success, responds with DescribeForecastExportJobOutput with field(s):
  • forecast_export_job_arn(Option<String>):

    The ARN of the forecast export job.

  • forecast_export_job_name(Option<String>):

    The name of the forecast export job.

  • forecast_arn(Option<String>):

    The Amazon Resource Name (ARN) of the exported forecast.

  • destination(Option<DataDestination>):

    The path to the Amazon Simple Storage Service (Amazon S3) bucket where the forecast is exported.

  • message(Option<String>):

    If an error occurred, an informational message about the error.

  • status(Option<String>):

    The status of the forecast export job. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

    The Status of the forecast export job must be ACTIVE before you can access the forecast in your S3 bucket.

  • creation_time(Option<DateTime>):

    When the forecast export job was created.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

  • format(Option<String>):

    The format of the exported data, CSV or PARQUET.

• On failure, responds with SdkError<DescribeForecastExportJobError>

impl Client

pub fn describe_monitor(&self) -> DescribeMonitorFluentBuilder

Constructs a fluent builder for the DescribeMonitor operation.

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

    The Amazon Resource Name (ARN) of the monitor resource to describe.

• On success, responds with DescribeMonitorOutput with field(s):
  • monitor_name(Option<String>):

    The name of the monitor.

  • monitor_arn(Option<String>):

    The Amazon Resource Name (ARN) of the monitor resource described.

  • resource_arn(Option<String>):

    The Amazon Resource Name (ARN) of the auto predictor being monitored.

  • status(Option<String>):

    The status of the monitor resource.

  • last_evaluation_time(Option<DateTime>):

    The timestamp of the latest evaluation completed by the monitor.

  • last_evaluation_state(Option<String>):

    The state of the monitor’s latest evaluation.

  • baseline(Option<Baseline>):

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

  • message(Option<String>):

    An error message, if any, for the monitor.

  • creation_time(Option<DateTime>):

    The timestamp for when the monitor resource was created.

  • last_modification_time(Option<DateTime>):

    The timestamp of the latest modification to the monitor.

  • estimated_evaluation_time_remaining_in_minutes(Option<i64>):

    The estimated number of minutes remaining before the monitor resource finishes its current evaluation.

• On failure, responds with SdkError<DescribeMonitorError>

impl Client

pub fn describe_predictor(&self) -> DescribePredictorFluentBuilder

Constructs a fluent builder for the DescribePredictor operation.

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

    The Amazon Resource Name (ARN) of the predictor that you want information about.

• On success, responds with DescribePredictorOutput with field(s):
  • predictor_arn(Option<String>):

    The ARN of the predictor.

  • predictor_name(Option<String>):

    The name of the predictor.

  • algorithm_arn(Option<String>):

    The Amazon Resource Name (ARN) of the algorithm used for model training.

  • auto_ml_algorithm_arns(Option<Vec<String>>):

    When PerformAutoML is specified, the ARN of the chosen algorithm.

  • forecast_horizon(Option<i32>):

    The number of time-steps of the forecast. The forecast horizon is also called the prediction length.

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

    The forecast types used during predictor training. Default value is [“0.1”,“0.5”,“0.9”]

  • perform_auto_ml(Option<bool>):

    Whether the predictor is set to perform AutoML.

  • auto_ml_override_strategy(Option<AutoMlOverrideStrategy>):

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

    The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy.

    This parameter is only valid for predictors trained using AutoML.

  • perform_hpo(Option<bool>):

    Whether the predictor is set to perform hyperparameter optimization (HPO).

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

    The default training parameters or overrides selected during model training. When running AutoML or choosing HPO with CNN-QR or DeepAR+, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.

  • 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(Option<HyperParameterTuningJobConfig>):

    The hyperparameter override values for the algorithm.

  • input_data_config(Option<InputDataConfig>):

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

  • featurization_config(Option<FeaturizationConfig>):

    The featurization configuration.

  • encryption_config(Option<EncryptionConfig>):

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

  • predictor_execution_details(Option<PredictorExecutionDetails>):

    Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You specify the number of backtests to perform when you call the operation.

  • estimated_time_remaining_in_minutes(Option<i64>):

    The estimated time remaining in minutes for the predictor training job to complete.

  • is_auto_predictor(Option<bool>):

    Whether the predictor was created with CreateAutoPredictor.

  • dataset_import_job_arns(Option<Vec<String>>):

    An array of the ARNs of the dataset import jobs used to import training data for the predictor.

  • status(Option<String>):

    The status of the predictor. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

    The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.

  • message(Option<String>):

    If an error occurred, an informational message about the error.

  • creation_time(Option<DateTime>):

    When the model training task was created.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

  • optimization_metric(Option<OptimizationMetric>):

    The accuracy metric used to optimize the predictor.

• On failure, responds with SdkError<DescribePredictorError>

impl Client

pub fn describe_predictor_backtest_export_job( &self ) -> DescribePredictorBacktestExportJobFluentBuilder

Constructs a fluent builder for the DescribePredictorBacktestExportJob operation.

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

    The Amazon Resource Name (ARN) of the predictor backtest export job.

• On success, responds with DescribePredictorBacktestExportJobOutput with field(s):
  • predictor_backtest_export_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the predictor backtest export job.

  • predictor_backtest_export_job_name(Option<String>):

    The name of the predictor backtest export job.

  • predictor_arn(Option<String>):

    The Amazon Resource Name (ARN) of the predictor.

  • destination(Option<DataDestination>):

    The destination for an export job. Provide an S3 path, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an Key Management Service (KMS) key (optional).

  • message(Option<String>):

    Information about any errors that may have occurred during the backtest export.

  • status(Option<String>):

    The status of the predictor backtest export job. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

  • creation_time(Option<DateTime>):

    When the predictor backtest export job was created.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

  • format(Option<String>):

    The format of the exported data, CSV or PARQUET.

• On failure, responds with SdkError<DescribePredictorBacktestExportJobError>

impl Client

pub fn describe_what_if_analysis(&self) -> DescribeWhatIfAnalysisFluentBuilder

Constructs a fluent builder for the DescribeWhatIfAnalysis operation.

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

    The Amazon Resource Name (ARN) of the what-if analysis that you are interested in.

• On success, responds with DescribeWhatIfAnalysisOutput with field(s):
  • what_if_analysis_name(Option<String>):

    The name of the what-if analysis.

  • what_if_analysis_arn(Option<String>):

    The Amazon Resource Name (ARN) of the what-if analysis.

  • forecast_arn(Option<String>):

    The Amazon Resource Name (ARN) of the what-if forecast.

  • estimated_time_remaining_in_minutes(Option<i64>):

    The approximate time remaining to complete the what-if analysis, in minutes.

  • status(Option<String>):

    The status of the what-if analysis. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

    The Status of the what-if analysis must be ACTIVE before you can access the analysis.

  • message(Option<String>):

    If an error occurred, an informational message about the error.

  • creation_time(Option<DateTime>):

    When the what-if analysis was created.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

  • 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 failure, responds with SdkError<DescribeWhatIfAnalysisError>

impl Client

pub fn describe_what_if_forecast(&self) -> DescribeWhatIfForecastFluentBuilder

Constructs a fluent builder for the DescribeWhatIfForecast operation.

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

    The Amazon Resource Name (ARN) of the what-if forecast that you are interested in.

• On success, responds with DescribeWhatIfForecastOutput with field(s):
  • what_if_forecast_name(Option<String>):

    The name of the what-if forecast.

  • what_if_forecast_arn(Option<String>):

    The Amazon Resource Name (ARN) of the what-if forecast.

  • what_if_analysis_arn(Option<String>):

    The Amazon Resource Name (ARN) of the what-if analysis that contains this forecast.

  • estimated_time_remaining_in_minutes(Option<i64>):

    The approximate time remaining to complete the what-if forecast, in minutes.

  • status(Option<String>):

    The status of the what-if forecast. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

    The Status of the what-if forecast must be ACTIVE before you can access the forecast.

  • message(Option<String>):

    If an error occurred, an informational message about the error.

  • creation_time(Option<DateTime>):

    When the what-if forecast was created.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

  • time_series_transformations(Option<Vec<TimeSeriesTransformation>>):

    An array of Action and TimeSeriesConditions elements that describe what transformations were applied to which time series.

  • time_series_replacements_data_source(Option<TimeSeriesReplacementsDataSource>):

    An array of S3Config, Schema, and Format elements that describe the replacement time series.

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

    The quantiles at which probabilistic forecasts are generated. You can specify up to five quantiles per what-if forecast in the CreateWhatIfForecast operation. If you didn’t specify quantiles, the default values are [“0.1”, “0.5”, “0.9”].

• On failure, responds with SdkError<DescribeWhatIfForecastError>

impl Client

pub fn describe_what_if_forecast_export( &self ) -> DescribeWhatIfForecastExportFluentBuilder

Constructs a fluent builder for the DescribeWhatIfForecastExport operation.

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

    The Amazon Resource Name (ARN) of the what-if forecast export that you are interested in.

• On success, responds with DescribeWhatIfForecastExportOutput with field(s):
  • what_if_forecast_export_arn(Option<String>):

    The Amazon Resource Name (ARN) of the what-if forecast export.

  • what_if_forecast_export_name(Option<String>):

    The name of the what-if forecast export.

  • what_if_forecast_arns(Option<Vec<String>>):

    An array of Amazon Resource Names (ARNs) that represent all of the what-if forecasts exported in this resource.

  • destination(Option<DataDestination>):

    The destination for an export job. Provide an S3 path, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an Key Management Service (KMS) key (optional).

  • message(Option<String>):

    If an error occurred, an informational message about the error.

  • status(Option<String>):

    The status of the what-if forecast. States include:

    • ACTIVE

    • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

    • CREATE_STOPPING, CREATE_STOPPED

    • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

    The Status of the what-if forecast export must be ACTIVE before you can access the forecast export.

  • creation_time(Option<DateTime>):

    When the what-if forecast export was created.

  • estimated_time_remaining_in_minutes(Option<i64>):

    The approximate time remaining to complete the what-if forecast export, in minutes.

  • last_modification_time(Option<DateTime>):

    The last time the resource was modified. The timestamp depends on the status of the job:

    • CREATE_PENDING - The CreationTime.

    • CREATE_IN_PROGRESS - The current timestamp.

    • CREATE_STOPPING - The current timestamp.

    • CREATE_STOPPED - When the job stopped.

    • ACTIVE or CREATE_FAILED - When the job finished or failed.

  • format(Option<String>):

    The format of the exported data, CSV or PARQUET.

• On failure, responds with SdkError<DescribeWhatIfForecastExportError>

impl Client

pub fn get_accuracy_metrics(&self) -> GetAccuracyMetricsFluentBuilder

Constructs a fluent builder for the GetAccuracyMetrics operation.

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

    The Amazon Resource Name (ARN) of the predictor to get metrics for.

• On success, responds with GetAccuracyMetricsOutput with field(s):
  • predictor_evaluation_results(Option<Vec<EvaluationResult>>):

    An array of results from evaluating the predictor.

  • is_auto_predictor(Option<bool>):

    Whether the predictor was created with CreateAutoPredictor.

  • auto_ml_override_strategy(Option<AutoMlOverrideStrategy>):

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

    The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy.

    This parameter is only valid for predictors trained using AutoML.

  • optimization_metric(Option<OptimizationMetric>):

    The accuracy metric used to optimize the predictor.

• On failure, responds with SdkError<GetAccuracyMetricsError>

impl Client

pub fn list_dataset_groups(&self) -> ListDatasetGroupsFluentBuilder

Constructs a fluent builder for the ListDatasetGroups 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.

• On success, responds with ListDatasetGroupsOutput with field(s):
  • dataset_groups(Option<Vec<DatasetGroupSummary>>):

    An array of objects that summarize each dataset group’s properties.

  • next_token(Option<String>):

    If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListDatasetGroupsError>

impl Client

pub fn list_dataset_import_jobs(&self) -> ListDatasetImportJobsFluentBuilder

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(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):
  • dataset_import_jobs(Option<Vec<DatasetImportJobSummary>>):

    An array of objects that summarize each dataset import job’s properties.

  • next_token(Option<String>):

    If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListDatasetImportJobsError>

impl Client

pub fn list_datasets(&self) -> ListDatasetsFluentBuilder

Constructs a fluent builder for the ListDatasets 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.

• On success, responds with ListDatasetsOutput with field(s):
  • datasets(Option<Vec<DatasetSummary>>):

    An array of objects that summarize each dataset’s properties.

  • next_token(Option<String>):

    If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListDatasetsError>

impl Client

pub fn list_explainabilities(&self) -> ListExplainabilitiesFluentBuilder

Constructs a fluent builder for the ListExplainabilities 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 returned in the response.

  • filters(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. Valid values are ResourceArn and Status.

    • Value - The value to match.

• On success, responds with ListExplainabilitiesOutput with field(s):
  • explainabilities(Option<Vec<ExplainabilitySummary>>):

    An array of objects that summarize the properties of each Explainability resource.

  • next_token(Option<String>):

    Returns this token if the response is truncated. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListExplainabilitiesError>

impl Client

pub fn list_explainability_exports( &self ) -> ListExplainabilityExportsFluentBuilder

Constructs a fluent builder for the ListExplainabilityExports 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(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 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. Valid values are ResourceArn and Status.

    • Value - The value to match.

• On success, responds with ListExplainabilityExportsOutput with field(s):
  • explainability_exports(Option<Vec<ExplainabilityExportSummary>>):

    An array of objects that summarize the properties of each Explainability export.

  • next_token(Option<String>):

    Returns this token if the response is truncated. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListExplainabilityExportsError>

impl Client

pub fn list_forecast_export_jobs(&self) -> ListForecastExportJobsFluentBuilder

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(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):
  • forecast_export_jobs(Option<Vec<ForecastExportJobSummary>>):

    An array of objects that summarize each export job’s properties.

  • next_token(Option<String>):

    If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListForecastExportJobsError>

impl Client

pub fn list_forecasts(&self) -> ListForecastsFluentBuilder

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(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):
  • forecasts(Option<Vec<ForecastSummary>>):

    An array of objects that summarize each forecast’s properties.

  • next_token(Option<String>):

    If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListForecastsError>

impl Client

pub fn list_monitor_evaluations(&self) -> ListMonitorEvaluationsFluentBuilder

Constructs a fluent builder for the ListMonitorEvaluations 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 monitoring results to return.

  • monitor_arn(impl Into<String>) / set_monitor_arn(Option<String>):

    The Amazon Resource Name (ARN) of the monitor resource to get results from.

  • filters(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 EvaluationState.

    • Value - The value to match. Valid values are only SUCCESS or FAILURE.

    For example, to list only successful monitor evaluations, you would specify:

    “Filters”: [ { “Condition”: “IS”, “Key”: “EvaluationState”, “Value”: “SUCCESS” } ]

• On success, responds with ListMonitorEvaluationsOutput with field(s):
  • next_token(Option<String>):

    If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.

  • predictor_monitor_evaluations(Option<Vec<PredictorMonitorEvaluation>>):

    The monitoring results and predictor events collected by the monitor resource during different windows of time.

    For information about monitoring see Viewing Monitoring Results. For more information about retrieving monitoring results see Viewing Monitoring Results.

• On failure, responds with SdkError<ListMonitorEvaluationsError>

impl Client

pub fn list_monitors(&self) -> ListMonitorsFluentBuilder

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(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):
  • monitors(Option<Vec<MonitorSummary>>):

    An array of objects that summarize each monitor’s properties.

  • next_token(Option<String>):

    If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListMonitorsError>

impl Client

pub fn list_predictor_backtest_export_jobs( &self ) -> ListPredictorBacktestExportJobsFluentBuilder

Constructs a fluent builder for the ListPredictorBacktestExportJobs 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(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 predictor backtest export jobs 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. To include the predictor backtest export jobs that match the statement, specify IS. To exclude matching predictor backtest export jobs, specify IS_NOT.

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

    • Value - The value to match.

• On success, responds with ListPredictorBacktestExportJobsOutput with field(s):
  • predictor_backtest_export_jobs(Option<Vec<PredictorBacktestExportJobSummary>>):

    An array of objects that summarize the properties of each predictor backtest export job.

  • next_token(Option<String>):

    Returns this token if the response is truncated. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListPredictorBacktestExportJobsError>

impl Client

pub fn list_predictors(&self) -> ListPredictorsFluentBuilder

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(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):
  • predictors(Option<Vec<PredictorSummary>>):

    An array of objects that summarize each predictor’s properties.

  • next_token(Option<String>):

    If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListPredictorsError>

impl Client

pub fn list_tags_for_resource(&self) -> ListTagsForResourceFluentBuilder

Constructs a fluent builder for the ListTagsForResource 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.

• On success, responds with ListTagsForResourceOutput with field(s):
  • tags(Option<Vec<Tag>>):

    The tags for the resource.

• On failure, responds with SdkError<ListTagsForResourceError>

impl Client

pub fn list_what_if_analyses(&self) -> ListWhatIfAnalysesFluentBuilder

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(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):
  • what_if_analyses(Option<Vec<WhatIfAnalysisSummary>>):

    An array of WhatIfAnalysisSummary objects that describe the matched analyses.

  • next_token(Option<String>):

    If the response is truncated, Forecast returns this token. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListWhatIfAnalysesError>

impl Client

pub fn list_what_if_forecast_exports( &self ) -> ListWhatIfForecastExportsFluentBuilder

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(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):
  • what_if_forecast_exports(Option<Vec<WhatIfForecastExportSummary>>):

    An array of WhatIfForecastExports objects that describe the matched forecast exports.

  • next_token(Option<String>):

    If the response is truncated, Forecast returns this token. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<ListWhatIfForecastExportsError>

impl Client

pub fn list_what_if_forecasts(&self) -> ListWhatIfForecastsFluentBuilder

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(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):
  • what_if_forecasts(Option<Vec<WhatIfForecastSummary>>):

    An array of WhatIfForecasts objects that describe the matched forecasts.

  • 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.

• On failure, responds with SdkError<ListWhatIfForecastsError>

impl Client

pub fn resume_resource(&self) -> ResumeResourceFluentBuilder

Constructs a fluent builder for the ResumeResource operation.

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

    The Amazon Resource Name (ARN) of the monitor resource to resume.

• On success, responds with ResumeResourceOutput
• On failure, responds with SdkError<ResumeResourceError>

impl Client

pub fn stop_resource(&self) -> StopResourceFluentBuilder

Constructs a fluent builder for the StopResource 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 to stop. The supported ARNs are DatasetImportJobArn, PredictorArn, PredictorBacktestExportJobArn, ForecastArn, ForecastExportJobArn, ExplainabilityArn, and ExplainabilityExportArn.

• On success, responds with StopResourceOutput
• On failure, responds with SdkError<StopResourceError>

impl Client

pub fn tag_resource(&self) -> TagResourceFluentBuilder

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(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 Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

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

impl Client

pub fn untag_resource(&self) -> UntagResourceFluentBuilder

Constructs a fluent builder for the UntagResource 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.

  • tag_keys(impl Into<String>) / set_tag_keys(Option<Vec<String>>):

    The keys of the tags to be removed.

• On success, responds with UntagResourceOutput
• On failure, responds with SdkError<UntagResourceError>

impl Client

pub fn update_dataset_group(&self) -> UpdateDatasetGroupFluentBuilder

Constructs a fluent builder for the UpdateDatasetGroup operation.

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

    The ARN of the dataset group.

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

    An array of the Amazon Resource Names (ARNs) of the datasets to add to the dataset group.

• On success, responds with UpdateDatasetGroupOutput
• On failure, responds with SdkError<UpdateDatasetGroupError>

impl Client

pub fn from_conf(conf: Config) -> Self

Creates a new client from the service Config.

Panics

This method will panic if the conf has retry or timeouts enabled without a sleep_impl. If you experience this panic, it can be fixed by setting the sleep_impl, or by disabling retries and timeouts.

pub fn config(&self) -> &Config

Returns the client’s configuration.

impl Client

pub fn new(sdk_config: &SdkConfig) -> Self

Creates a new client from an SDK Config.

Panics

• This method will panic if the sdk_config is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
• This method will panic if the sdk_config is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.

Trait Implementations

impl Clone for Client

fn clone(&self) -> Client

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Client

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.