Example of Trend Analysis
main topic     interpreting results     session command     see also 

You collect employment data in a trade business over 60 months and wish to predict employment for the next 12 months. Because there is an overall curvilinear pattern to the data, you use trend analysis and fit a quadratic trend model. Because there is also a seasonal component, you save the fits and residuals to perform decomposition of the residuals (see Example of Decomposition).

1    Open the worksheet EMPLOY.MTW.

2    Choose Stat > Time Series > Trend Analysis.

3    In Variable, enter Trade.

4    Under Model Type, choose Quadratic.

5    Check Generate forecasts and enter 12 in Number of forecasts.

6    Click Storage.

7    Check Fits (Trend Line), Residuals (detrended data), and Forecasts. Click OK in each dialog box.

Session Window Output

Trend Analysis for Trade

 

 

Data      Trade

Length    60

NMissing  0

 

 

Fitted Trend Equation

 

Yt = 320.76 + 0.509×t + 0.01075×t^2

 

 

Accuracy Measures

 

MAPE   1.7076

MAD    5.9566

MSD   59.1305

 

 

Forecasts

 

Period  Forecast

61       391.818

62       393.649

63       395.502

64       397.376

65       399.271

66       401.188

67       403.127

68       405.087

69       407.068

70       409.071

71       411.096

72       413.142

Graph window output

Interpreting the results

The trend plot that shows the original data, the fitted trend line, and forecasts. The Session window output also displays the fitted trend equation and three Measures of Accuracy to help you determine the accuracy of the fitted values: MAPE, MAD, and MSD. The trade employment data show a general upward trend, though with an evident seasonal component. The trend model appears to fit well to the overall trend, but the seasonal pattern is not well fit. To better fit these data, you also use decomposition on the stored residuals and add the trend analysis and decomposition fits and forecasts (see Example of decomposition).