Struct TimeSeriesForecastingJobConfig

#[non_exhaustive]
pub struct TimeSeriesForecastingJobConfig {
    pub feature_specification_s3_uri: Option<String>,
    pub completion_criteria: Option<AutoMlJobCompletionCriteria>,
    pub forecast_frequency: Option<String>,
    pub forecast_horizon: Option<i32>,
    pub forecast_quantiles: Option<Vec<String>>,
    pub transformations: Option<TimeSeriesTransformations>,
    pub time_series_config: Option<TimeSeriesConfig>,
    pub holiday_config: Option<Vec<HolidayConfigAttributes>>,
    pub candidate_generation_config: Option<CandidateGenerationConfig>,
}

The collection of settings used by an AutoML job V2 for the time-series forecasting problem type.

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.

feature_specification_s3_uri: Option<String>

A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig.

You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":\["col1", "col2", ...\] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

Autopilot supports the following data types: numeric, categorical, text, and datetime.

These column keys must not include any column set in TimeSeriesConfig.

completion_criteria: Option<AutoMlJobCompletionCriteria>

How long a job is allowed to run, or how many candidates a job is allowed to generate.

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. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min.

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

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 time-steps in the dataset.

forecast_quantiles: Option<Vec<String>>

The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.

transformations: Option<TimeSeriesTransformations>

The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.

time_series_config: Option<TimeSeriesConfig>

The collection of components that defines the time-series.

holiday_config: Option<Vec<HolidayConfigAttributes>>

The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.

candidate_generation_config: Option<CandidateGenerationConfig>

Stores the configuration information for how model candidates are generated using an AutoML job V2.

Implementations

impl TimeSeriesForecastingJobConfig

pub fn feature_specification_s3_uri(&self) -> Option<&str>

A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig.

You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":\["col1", "col2", ...\] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

Autopilot supports the following data types: numeric, categorical, text, and datetime.

These column keys must not include any column set in TimeSeriesConfig.

pub fn completion_criteria(&self) -> Option<&AutoMlJobCompletionCriteria>

How long a job is allowed to run, or how many candidates a job is allowed to generate.

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. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min.

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

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 time-steps in the dataset.

pub fn forecast_quantiles(&self) -> &[String]

The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.

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_quantiles.is_none().

pub fn transformations(&self) -> Option<&TimeSeriesTransformations>

The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.

pub fn time_series_config(&self) -> Option<&TimeSeriesConfig>

The collection of components that defines the time-series.

pub fn holiday_config(&self) -> &[HolidayConfigAttributes]

The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.

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

pub fn candidate_generation_config(&self) -> Option<&CandidateGenerationConfig>

Stores the configuration information for how model candidates are generated using an AutoML job V2.

impl TimeSeriesForecastingJobConfig

pub fn builder() -> TimeSeriesForecastingJobConfigBuilder

Creates a new builder-style object to manufacture TimeSeriesForecastingJobConfig.

Trait Implementations

impl Clone for TimeSeriesForecastingJobConfig

fn clone(&self) -> TimeSeriesForecastingJobConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TimeSeriesForecastingJobConfig

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

Formats the value using the given formatter.

impl PartialEq for TimeSeriesForecastingJobConfig

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

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

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 TimeSeriesForecastingJobConfig