Example of Single Exponential Smoothing
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You wish to predict employment over 6 months in a segment of the metals industry using data collected over 60 months. You use single exponential smoothing because there is no clear trend or seasonal pattern in the data.

1    Open the worksheet EMPLOY.MTW.

2    Choose Stat > Time Series > Single Exp Smoothing.

3    In Variable, enter Metals.

4    Check Generate forecasts, and enter 6 in Number of forecasts. Click OK.

Session window output

Single Exponential Smoothing for Metals

 

 

 

 

Data    Metals

Length  60

 

 

Smoothing Constant

 

α  1.04170

 

 

Accuracy Measures

 

MAPE  1.11648

MAD   0.50427

MSD   0.42956

 

 

Forecasts

 

Period  Forecast    Lower    Upper

61       48.0560  46.8206  49.2914

62       48.0560  46.8206  49.2914

63       48.0560  46.8206  49.2914

64       48.0560  46.8206  49.2914

65       48.0560  46.8206  49.2914

66       48.0560  46.8206  49.2914

Graph window output

Interpreting the results

Minitab generated the default time series plot which displays the series and fitted values (one-period-ahead forecasts), along with the six forecasts.

In both the Session and Graph windows, Minitab displays the smoothing constant (weight) used and three measures to help you to determine the accuracy of the fitted values: MAPE, MAD, and MSD (see Measures of accuracy). The three accuracy measures, MAPE, MAD, and MSD, were 1.12, 0.50, and 0.43, respectively for the single exponential smoothing model, compared to 1.55, 0.70, and 0.76, respectively, for the moving average fit (see Example of moving average). Because these values are smaller for single exponential smoothing, you can judge that this method provides a better fit to these data.