Determining and Setting the Periodicity
The Seasonal Decomposition procedure requires the presence of a periodic date component in the active dataset—for example, a yearly periodicity of 12 (months), a weekly periodicity of 7 (days), and so on. It's a good idea to plot your time series first, because viewing a time series plot often leads to a reasonable guess about the underlying periodicity.
To obtain a plot of men's clothing sales over time:
- From the menus choose:
Figure 1. Sequence Charts dialog box 
- Select Sales of Men's Clothing and move it into the Variables list.
- Select Date and move it into the Time Axis Labels list.
- Click OK.
Figure 2. Sales of men's clothing (in U.S. dollars) 
The series exhibits a number of peaks, but they do not appear to be equally spaced. This output suggests that if the series has a periodic component, it also has fluctuations that are not periodic--the typical case for real-time series. Aside from the small-scale fluctuations, the significant peaks appear to be separated by more than a few months. Given the seasonal nature of sales, with typical highs during the December holiday season, the time series probably has an annual periodicity. Also notice that the seasonal variations appear to grow with the upward series trend, suggesting that the seasonal variations may be proportional to the level of the series, which implies a multiplicative model rather than an additive model.
Examining the autocorrelations and partial autocorrelations of a time series provides a more quantitative conclusion about the underlying periodicity.
- From the menus choose:
Figure 3. Autocorrelations dialog box 
- Select Sales of Men's Clothing and move it into the Variables list.
- Click OK.
Figure 4. Autocorrelation plot for men 
The autocorrelation function shows a significant peak at a lag of 1 with a long exponential tail—a typical pattern for time series. The significant peak at a lag of 12 suggests the presence of an annual seasonal component in the data. Examination of the partial autocorrelation function will allow a more definitive conclusion.
Figure 5. Partial autocorrelation plot for men 
The significant peak at a lag of 12 in the partial autocorrelation function confirms the presence of an annual seasonal component in the data.
To set an annual periodicity:
- From the menus choose:
Figure 6. Define Dates dialog box 
- Select Years, months in the Cases Are list.
- Enter 1989 for the year and 1 for the month.
- Click OK.
This sets the periodicity to 12 and creates a set of date variables that are designed to work with Forecasting procedures.