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DecompositionStat > Time Series > Decomposition
You can use decomposition to separate the time series into linear trend and seasonal components, as well as error, and provide forecasts. You can choose whether the seasonal component is additive or multiplicative with the trend. Use this procedure when you wish to forecast and there is a seasonal component to your series, or if you simply want to examine the nature of the component parts. See [6] for a discussion of decomposition methods.
Variable: Enter the column containing the time series.
Seasonal Length: Enter a positive integer greater than or equal to 2. This is the length of the seasonal component. For example, if you have monthly data, you might use a seasonal length of 12.
Model Type:
Multiplicative: Choose to use the multiplicative model.
Additive: Choose to use the additive model.
Model Components:
Trend plus seasonal: Choose to include the trend component in the decomposition.
Seasonal only: Choose to omit the trend component from the decomposition. You might want to do this if you have already detrended your data with Trend Analysis.
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Caution |
If the data contain a trend component but you omit it from the decomposition, the estimates of the seasonal indices may be affected. |
Generate forecasts: Check if you want to generate forecasts.
Number of forecasts: Enter an integer to indicate how the number of forecasts.
Starting from origin: Enter a positive integer to specify a starting point for the forecasts. For example, if you specify 4 forecasts and 48 for the origin, Minitab computes forecasts for periods 49, 50, 51, and 52. If you leave this space blank, Minitab generates forecasts from the end of the data.