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Class: Aws::ForecastService::Types::FeaturizationConfig
- Inherits:
-
Struct
- Object
- Struct
- Aws::ForecastService::Types::FeaturizationConfig
- Defined in:
- (unknown)
Overview
When passing FeaturizationConfig as input to an Aws::Client method, you can use a vanilla Hash:
{
forecast_frequency: "Frequency", # required
forecast_dimensions: ["Name"],
featurizations: [
{
attribute_name: "Name", # required
featurization_pipeline: [
{
featurization_method_name: "filling", # required, accepts filling
featurization_method_parameters: {
"ParameterKey" => "ParameterValue",
},
},
],
},
],
}
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.
Returned by:
Instance Attribute Summary collapse
-
#featurizations ⇒ Array<Types::Featurization>
An array of featurization (transformation) information for the fields of a dataset.
-
#forecast_dimensions ⇒ Array<String>
An array of dimension (field) names that specify how to group the generated forecast.
-
#forecast_frequency ⇒ String
The frequency of predictions in a forecast.
Instance Attribute Details
#featurizations ⇒ Array<Types::Featurization>
An array of featurization (transformation) information for the fields of a dataset.
#forecast_dimensions ⇒ Array<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.