Class: Aws::ForecastService::Types::FeaturizationConfig
- Inherits:
-
Struct
- Object
- Struct
- Aws::ForecastService::Types::FeaturizationConfig
- Defined in:
- gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb
Overview
When making an API call, you may pass FeaturizationConfig data as a 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.
Constant Summary collapse
- SENSITIVE =
[]
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.
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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 4254 class FeaturizationConfig < Struct.new( :forecast_frequency, :forecast_dimensions, :featurizations) SENSITIVE = [] include Aws::Structure end |
#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.
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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 4254 class FeaturizationConfig < Struct.new( :forecast_frequency, :forecast_dimensions, :featurizations) SENSITIVE = [] include Aws::Structure end |
#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.
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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 4254 class FeaturizationConfig < Struct.new( :forecast_frequency, :forecast_dimensions, :featurizations) SENSITIVE = [] include Aws::Structure end |