Struct CreateAutoPredictorInput

#[non_exhaustive]
pub struct CreateAutoPredictorInput {

    pub predictor_name: Option<String>,
    pub forecast_horizon: Option<i32>,
    pub forecast_types: Option<Vec<String>>,
    pub forecast_dimensions: Option<Vec<String>>,
    pub forecast_frequency: Option<String>,
    pub data_config: Option<DataConfig>,
    pub encryption_config: Option<EncryptionConfig>,
    pub reference_predictor_arn: Option<String>,
    pub optimization_metric: Option<OptimizationMetric>,
    pub explain_predictor: Option<bool>,
    pub tags: Option<Vec<Tag>>,
    pub monitor_config: Option<MonitorConfig>,
    pub time_alignment_boundary: Option<TimeAlignmentBoundary>,
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

predictor_name: Option<String>

A unique name for the predictor

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: 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: 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: 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: 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_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: Option<OptimizationMetric>

The accuracy metric used to optimize the predictor.

explain_predictor: Option<bool>

Create an Explainability resource for the predictor.

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: 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: 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.

Implementations

impl CreateAutoPredictorInput

pub fn predictor_name(&self) -> Option<&str>

A unique name for the predictor

pub fn forecast_horizon(&self) -> 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.

pub fn forecast_types(&self) -> &[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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .forecast_types.is_none().

pub fn forecast_dimensions(&self) -> &[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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .forecast_dimensions.is_none().

pub fn forecast_frequency(&self) -> Option<&str>

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.

pub fn data_config(&self) -> Option<&DataConfig>

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

pub fn encryption_config(&self) -> 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.

pub fn reference_predictor_arn(&self) -> Option<&str>

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) -> Option<&OptimizationMetric>

The accuracy metric used to optimize the predictor.

pub fn explain_predictor(&self) -> Option<bool>

Create an Explainability resource for the predictor.

pub fn tags(&self) -> &[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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .tags.is_none().

pub fn monitor_config(&self) -> 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.

pub fn time_alignment_boundary(&self) -> 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.

impl CreateAutoPredictorInput

pub fn builder() -> CreateAutoPredictorInputBuilder

Creates a new builder-style object to manufacture CreateAutoPredictorInput.

Trait Implementations

impl Clone for CreateAutoPredictorInput

fn clone(&self) -> CreateAutoPredictorInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAutoPredictorInput

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

Formats the value using the given formatter.

impl PartialEq for CreateAutoPredictorInput

fn eq(&self, other: &CreateAutoPredictorInput) -> bool

Tests for self and other values to be equal, and is used by ==.

const fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for CreateAutoPredictorInput