Struct aws_sdk_forecast::client::fluent_builders::CreateWhatIfForecast

pub struct CreateWhatIfForecast { /* private fields */ }

Fluent builder constructing a request to CreateWhatIfForecast.

A what-if forecast is a forecast that is created from a modified version of the baseline forecast. Each what-if forecast incorporates either a replacement dataset or a set of transformations to the original dataset.

Implementations

impl CreateWhatIfForecast

pub async fn customize(
    self
) -> Result<CustomizableOperation<CreateWhatIfForecast, AwsResponseRetryClassifier>, SdkError<CreateWhatIfForecastError>>

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<CreateWhatIfForecastOutput, SdkError<CreateWhatIfForecastError>>

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 what_if_forecast_name(self, input: impl Into<String>) -> Self

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

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

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

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

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

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

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

pub fn time_series_transformations(self, input: TimeSeriesTransformation) -> Self

Appends an item to TimeSeriesTransformations.

To override the contents of this collection use set_time_series_transformations.

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.

pub fn set_time_series_transformations(
    self,
    input: Option<Vec<TimeSeriesTransformation>>
) -> Self

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.

pub fn time_series_replacements_data_source(
    self,
    input: TimeSeriesReplacementsDataSource
) -> Self

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.

pub fn set_time_series_replacements_data_source(
    self,
    input: Option<TimeSeriesReplacementsDataSource>
) -> Self

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.

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

Appends an item to Tags.

To override the contents of this collection use set_tags.

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

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

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

Trait Implementations

impl Clone for CreateWhatIfForecast

fn clone(&self) -> CreateWhatIfForecast

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateWhatIfForecast

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

Formats the value using the given formatter.