Seasonal decomposition
Splits a series into trend, seasonal and irregular parts using the classical ratio- (multiplicative) or difference-to-moving-average (additive) method.
Menu: Analyze → Forecasting → Seasonal Decomposition…
When should I use it?
- Monthly or quarterly data with a repeating seasonal pattern.
What are the assumptions?
- At least two full seasonal cycles; positive values for the multiplicative model.
How do I run it in StatLab?
- Choose the series, its periodicity (12 = monthly, 4 = quarterly) and the model.
How do I read the output?
| Output | Meaning |
|---|---|
| Seasonal Factor | Multiplicative: 110% = 10% above trend in that period. Additive: units above trend. |
| Seasonally Adjusted | Series with the seasonal effect removed. |
Worked example
On the practice survey dataset (File → Sample dataset), this command — which the dialog's Paste button also produces — runs the procedure:
PROC tsSeasonal /v=score /period=12 /model=multiplicative /save=NO.
Output tables: Seasonal Factors · Decomposition.
Computed by StatLab's validated procedure code when this page was built. The example only illustrates the steps; choose variables that fit your own research question.
What are common mistakes?
- Using the additive model when the seasonal swing grows with the level of the series.