Data Preparation for Exponential Smoothing Models

Prepare your data for exponential smoothing by providing input data, aggregation methods, and model build parameters.

To build an ESM model, you must supply the following :

• Input data

• An aggregation level and method, if the case id is a date type

• Partitioning column, if the data are partitioned

In addition, for a greater control over the build process, the user may optionally specify model build parameters, all of which have defaults:

• Model

• Error type

• Optimization criterion

• Forecast Window

• Confidence level for forecast bounds

• Missing value handling

• Whether the input series is evenly spaced

See Also:

DBMS_DATA_MINING —Algorithm Settings: Exponential Smoothing Models for a listing and explanation of the available model settings.

Note:

The term hyperparameter is also interchangeably used for model setting.