Struct rusoto_forecast::FeaturizationConfig

pub struct FeaturizationConfig {
    pub featurizations: Option<Vec<Featurization>>,
    pub forecast_dimensions: Option<Vec<String>>,
    pub forecast_frequency: String,
}

In a CreatePredictor operation, the specified algorithm trains a model using the specified dataset group. You can optionally tell the operation to modify data fields prior to training a model. These modifications are referred to as featurization.

You define featurization using the FeaturizationConfig object. You specify an array of transformations, one for each field that you want to featurize. You then include the FeaturizationConfig object in your CreatePredictor request. Amazon Forecast applies the featurization to the TARGET_TIME_SERIES and RELATED_TIME_SERIES datasets before model training.

You can create multiple featurization configurations. For example, you might call the CreatePredictor operation twice by specifying different featurization configurations.

Fields

featurizations: Option<Vec<Featurization>>

An array of featurization (transformation) information for the fields of a dataset.

forecast_dimensions: Option<Vec<String>>

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

For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.

All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.

forecast_frequency: 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.

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.

Trait Implementations

impl Clone for FeaturizationConfig

fn clone(&self) -> FeaturizationConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for FeaturizationConfig

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

Formats the value using the given formatter.

impl Default for FeaturizationConfig

fn default() -> FeaturizationConfig

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for FeaturizationConfig

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl PartialEq<FeaturizationConfig> for FeaturizationConfig

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

This method tests for self and other values to be equal, and is used by ==.

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

This method tests for !=.

impl Serialize for FeaturizationConfig

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for FeaturizationConfig