FeaturizationConfig
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.
Contents
- Featurizations
-
An array of featurization (transformation) information for the fields of a dataset.
Type: Array of Featurization objects
Array Members: Minimum number of 1 item. Maximum number of 50 items.
Required: No
- ForecastDimensions
-
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 specifystore_id
as the dimension.All forecast dimensions specified in the
TARGET_TIME_SERIES
dataset don't need to be specified in theCreatePredictor
request. All forecast dimensions specified in theRELATED_TIME_SERIES
dataset must be specified in theCreatePredictor
request.Type: Array of strings
Array Members: Minimum number of 1 item. Maximum number of 5 items.
Length Constraints: Minimum length of 1. Maximum length of 63.
Pattern:
^[a-zA-Z][a-zA-Z0-9_]*
Required: No
- ForecastFrequency
-
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.
Type: String
Pattern:
^Y|M|W|D|H|30min|15min|10min|5min|1min$
Required: Yes
See Also
For more information about using this API in one of the language-specific AWS SDKs, see the following: