Holt-Winters
Synopsis
This operator trains an Holt-Winters forecasting model with triple exponential smoothing.
Description
The Holt-Winters forecasting method applies a triple exponential smoothing for level, trend and seasonal components.
A Holt-Winters model is defined by its threeorderparameters, alpha, beta, gamma. Alpha specifies the coefficient for the level smoothing. Beta specifies the coefficient for the trend smoothing. Gamma specifies the coefficient for the seasonal smoothing. There is also a parameter for the type of seasonality: Additive seasonality, where each season changes by a constant number. Multiplicative seasonality, where each season changes by a factor.
This operator works only on numerical time series.
Differentiation
This operator is similar to other modeling operators, but is specifically designed to work on time series data. One of the implications of this is, that the forecast model should be applied on the same data it was trained on.
Apply Forecast
This operator receives a trained Forecast Model (e.g. the Holt-Winters model) and creates the forecast for the time series it was trained on.
ARIMA
This operator trains an ARIMA model (Autoregressive Integrated Moving Average) on time series data to perform a forecast.
Default Forecast
This operator trains a Default Forecast model (predicting single value) on time series data to perform a forecast.
Function and Seasonal Component Forecast
This operator trains a Function and Seasonal Forecast model (combining fitted function and seasonal component values) on time series data to perform a forecast.
Input
example set
The ExampleSet which contains the time series data as an attribute.
Output
forecast model
The Holt-Winters model (forecast model) fitted to the specified time series attribute. It also contains the original time series values.
original
The ExampleSet that was given as input is passed through without changes.
Parameters
Time series attribute
The time series attribute (numerical) for which the Holt-Winters model should be build. The required attribute can be selected from this option. The attribute name can be selected from the drop down box of the parameter if the meta data is known.
Has indices
This parameter indicates if there is an index attribute associated with the time series. If this parameter is set to true, the index attribute has to be selected.
Indices attribute
If the parameterhas indicesis set to true, this parameter defines the associated index attribute. It can be either a date, date_time or numeric value type attribute. The attribute name can be selected from the drop down box of the parameter if the meta data is known.
Alpha: coefficient for level smoothing
The parameteralphaspecifies the coefficient for the level smoothing.
Beta: coefficient for trend smoothing
The parameterbetaspecifies the coefficient for the trend smoothing.
Gamma: coefficient for seasonality smoothing
The parametergammaspecifies the coefficient for the seasonal smoothing.
Period: length of one period
This parameter specifies the length of one seasonal period in the data. For weekly data this is often 7, for monthly data 12. The period has to be smaller or equal to half of the length of the time series.
Seasonality model
This parameter specifies the type of seasonality.
- additive: Additive seasonality, where each season changes by a constant number.
- multiplicative: Multiplicative seasonality, where each season changes by a factor.