Example of Moving Average
main topic     interpreting results     session command     see also 

You wish to predict employment over the next 6 months in a segment of the metals industry using data collected over 60 months. You use the moving average method as there is no well-defined trend or seasonal pattern in the data.

1    Open the worksheet EMPLOY.MTW.

2    Choose Stat > Time Series > Moving Average.

3    In Variable, enter Metals. In MA length, enter 3.

4    Check Center the moving averages.

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

Session window output

Moving Average for Metals

 

 

Data      Metals

Length    60

NMissing  0

 

 

Moving Average

 

Length  3

 

 

Accuracy Measures

 

MAPE  0.890317

MAD   0.402299

MSD   0.255287

 

 

Forecasts

 

Period  Forecast    Lower    Upper

61          49.2  48.2097  50.1903

62          49.2  48.2097  50.1903

63          49.2  48.2097  50.1903

64          49.2  48.2097  50.1903

65          49.2  48.2097  50.1903

66          49.2  48.2097  50.1903

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. Notice that the fitted value pattern lags behind the data pattern. This is because the fitted values are the moving averages from the previous time unit. If you wish to visually inspect how moving averages fit your data, plot the smoothed values rather than the predicted values.

To see exponential smoothing methods applied to the same data, see Example of single exponential smoothing and Example of double exponential smoothing.

In the Session window, Minitab displays three measures to help you determine the accuracy of the fitted values: MAPE, MAD, and MSD. See Measures of accuracy. Minitab also displays the forecasts along with the corresponding lower and upper 95% prediction limits.