Chapter Contents |
Previous |
Next |

Forecasting Process Details |

The smoothing equation is

The error-correction form of the smoothing equation is

(Note: For missing values, *e*_{t}=0.)

The *k*-step prediction equation is

The ARIMA model equivalency to simple exponential smoothing is the ARIMA(0,1,1) model

The moving-average form of the equation is

For simple exponential smoothing, the additive-invertible region is

The variance of the prediction errors is estimated as

The smoothing equations are

This method may be equivalently described in terms of two successive applications of simple exponential smoothing:

where *S*_{t}^{[1]} are the smoothed
values of *Y*_{t} and *S*_{t}^{[2]}
are the smoothed values of *S*_{t}^{[1]}.
The prediction equation then takes the form:

The error-correction form of the smoothing equations is

(Note: For missing values, *e*_{t}=0.)

The *k*-step prediction equation is

The ARIMA model equivalency to double exponential smoothing is the ARIMA(0,2,2) model

The moving-average form of the equation is

For double exponential smoothing, the additive-invertible region is

The variance of the prediction errors is estimated as

The smoothing equations are

The error-correction form of the smoothing equations is

(Note: For missing values, *e*_{t}=0.)

The *k*-step prediction equation is

The ARIMA model equivalency to linear exponential smoothing is the ARIMA(0,2,2) model

The moving-average form of the equation is

For linear exponential smoothing, the additive-invertible region is

The variance of the prediction errors is estimated as

The smoothing equations are

The error-correction form of the smoothing equations is

(Note: For missing values, *e*_{t}=0.)

The *k*-step prediction equation is

The ARIMA model equivalency to damped-trend linear exponential smoothing is the ARIMA(1,1,2) model

The moving-average form of the equation (assuming ) is

For damped-trend linear exponential smoothing, the additive-invertible region is

The variance of the prediction errors is estimated as

The smoothing equations are

The error-correction form of the smoothing equations is

(Note: For missing values, *e*_{t}=0.)

The *k*-step prediction equation is

The ARIMA model equivalency to seasonal exponential smoothing is
the ARIMA(0,1,p+1)(0,1,0)_{p} model

The moving-average form of the equation is

For seasonal exponential smoothing, the additive-invertible region is

The variance of the prediction errors is estimated as

The smoothing equations are

The error-correction form of the smoothing equations is

(Note: For missing values, *e*_{t}=0.)

The *k*-step prediction equation is

The ARIMA model equivalency to the additive version of Winters method
is the ARIMA(0,1,p+1)(0,1,0)_{p} model

The moving-average form of the equation is

For the additive version of Winters method (see Archibald 1990), the additive-invertible region is

where is the smallest non-negative solution to the equations listed in Archibald (1990).

The variance of the prediction errors is estimated as

The model equation for the multiplicative version of Winters method is

The smoothing equations are

The error-correction form of the smoothing equations is

(Note: For missing values, *e*_{t}=0.)

The *k*-step prediction equation is

The multiplicative version of Winters method does not have an ARIMA equivalent; however, when the seasonal variation is small, the ARIMA additive-invertible region of the additive version of Winters method described in the preceding section can approximate the stability region of the multiplicative version.

The variance of the prediction errors is estimated as

where are as described for the additive version of Winters method, and for .

Chapter Contents |
Previous |
Next |
Top |

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.