Example Winters' Method
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You wish to predict employment for the next six months in a food preparation industry using data collected over the last 60 months. You use Winters' method with the default multiplicative model, because there is a seasonal component, and possibly trend, apparent in the data.

1    Open the worksheet EMPLOY.MTW.

2    Choose Stat > Time Series > Winters' Method.

3    In Variable, enter Food. In Seasonal length, enter 12.

4    Under Model Type, choose Multiplicative.

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

Session window output

Winters’ Method for Food

 

 

Multiplicative Method

 

 

Data    Food

Length  60

 

 

Smoothing Constants

 

α (level)     0.2

γ (trend)     0.2

δ (seasonal)  0.2

 

 

Accuracy Measures

 

MAPE  1.88377

MAD   1.12068

MSD   2.86696

 

 

Forecasts

 

Period  Forecast    Lower    Upper

61       57.8102  55.0646  60.5558

62       57.3892  54.6006  60.1778

63       57.8332  54.9966  60.6698

64       57.9307  55.0414  60.8199

65       58.8311  55.8847  61.7775

66       62.7415  59.7339  65.7492

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 constants (weights) used for level, trend, and seasonal components used and three measures to help you determine the accuracy of the fitted values: MAPE, MAD, and MSD (see Measures of accuracy).

For these data, MAPE, MAD, and MSD were 1.88, 1.12, and 2.87, respectively, with the multiplicative model. MAPE, MAD, and MSD were 1.95, 1.15, and 2.67, respectively (output not shown) with the additive model, indicating that the multiplicative model provided a slightly better fit according to two of the three accuracy measures.