Struct aws_sdk_forecast::client::fluent_builders::CreateAutoPredictor

pub struct CreateAutoPredictor { /* private fields */ }

Fluent builder constructing a request to CreateAutoPredictor.

Creates an Amazon Forecast predictor.

Amazon Forecast creates predictors with AutoPredictor, which involves applying the optimal combination of algorithms to each time series in your datasets. You can use CreateAutoPredictor to create new predictors or upgrade/retrain existing predictors.

Creating new predictors

The following parameters are required when creating a new predictor:

• PredictorName - A unique name for the predictor.

• DatasetGroupArn - The ARN of the dataset group used to train the predictor.

• ForecastFrequency - The granularity of your forecasts (hourly, daily, weekly, etc).

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

When creating a new predictor, do not specify a value for ReferencePredictorArn.

Upgrading and retraining predictors

The following parameters are required when retraining or upgrading a predictor:

• PredictorName - A unique name for the predictor.

• ReferencePredictorArn - The ARN of the predictor to retrain or upgrade.

When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and PredictorName.

Implementations

impl CreateAutoPredictor

pub async fn customize(
    self
) -> Result<CustomizableOperation<CreateAutoPredictor, AwsResponseRetryClassifier>, SdkError<CreateAutoPredictorError>>

Consume this builder, creating a customizable operation that can be modified before being sent. The operation’s inner http::Request can be modified as well.

pub async fn send(
    self
) -> Result<CreateAutoPredictorOutput, SdkError<CreateAutoPredictorError>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn predictor_name(self, input: impl Into<String>) -> Self

A unique name for the predictor

pub fn set_predictor_name(self, input: Option<String>) -> Self

A unique name for the predictor

pub fn forecast_horizon(self, input: i32) -> Self

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.

pub fn set_forecast_horizon(self, input: Option<i32>) -> Self

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.

pub fn forecast_types(self, input: impl Into<String>) -> Self

Appends an item to ForecastTypes.

To override the contents of this collection use set_forecast_types.

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.

pub fn set_forecast_types(self, input: Option<Vec<String>>) -> Self

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.

pub fn forecast_dimensions(self, input: impl Into<String>) -> Self

Appends an item to ForecastDimensions.

To override the contents of this collection use set_forecast_dimensions.

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.

pub fn set_forecast_dimensions(self, input: Option<Vec<String>>) -> Self

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.

pub fn forecast_frequency(self, input: impl Into<String>) -> Self

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.

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.

pub fn set_forecast_frequency(self, input: Option<String>) -> Self

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.

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.

pub fn data_config(self, input: DataConfig) -> Self

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

pub fn set_data_config(self, input: Option<DataConfig>) -> Self

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

pub fn encryption_config(self, input: EncryptionConfig) -> Self

An AWS Key Management Service (KMS) key and an AWS 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.

pub fn set_encryption_config(self, input: Option<EncryptionConfig>) -> Self

An AWS Key Management Service (KMS) key and an AWS 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.

pub fn reference_predictor_arn(self, input: impl Into<String>) -> Self

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.

pub fn set_reference_predictor_arn(self, input: Option<String>) -> Self

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.

pub fn optimization_metric(self, input: OptimizationMetric) -> Self

The accuracy metric used to optimize the predictor.

pub fn set_optimization_metric(self, input: Option<OptimizationMetric>) -> Self

The accuracy metric used to optimize the predictor.

pub fn explain_predictor(self, input: bool) -> Self

Create an Explainability resource for the predictor.

pub fn set_explain_predictor(self, input: Option<bool>) -> Self

Create an Explainability resource for the predictor.

pub fn tags(self, input: Tag) -> Self

Appends an item to Tags.

To override the contents of this collection use set_tags.

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.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

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.

pub fn monitor_config(self, input: MonitorConfig) -> Self

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.

pub fn set_monitor_config(self, input: Option<MonitorConfig>) -> Self

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.

pub fn time_alignment_boundary(self, input: TimeAlignmentBoundary) -> Self

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.

pub fn set_time_alignment_boundary(
    self,
    input: Option<TimeAlignmentBoundary>
) -> Self

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.

Trait Implementations

impl Clone for CreateAutoPredictor

fn clone(&self) -> CreateAutoPredictor

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAutoPredictor

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

Formats the value using the given formatter.