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How do you seasonally adjust time series data?
Time Series Analysis: Seasonal Adjustment Methods
- Estimate the trend by a moving average.
- Remove the trend leaving the seasonal and irregular components.
- Estimate the seasonal component using moving averages to smooth out the irregulars.
How do you adjust data for seasonality?
We call these averages “seasonal factors.” To seasonally adjust your data, divide each data point by the seasonal factor for its month. If January’s average ratio is 0.85, it means that January runs about 15 percent below normal.
How does seasonal adjustment affect year over year comparisons?
Year-over-year comparisons are made more accurate when using seasonally-adjusted data because seasonal adjustment controls for calendar effects and data outliers. Because seasonal adjustment removes seasonal effects, data can be compared across months and years directly. This comparison can be misleading with unadjusted data.
What does seasonal adjustment remove from a time series?
Seasonal adjustment removes seasonal effects. The seasonal effect in a time-series is any effect that is reasonably stable in terms of annual timing, direction, and magnitude. This includes changes brought about by the seasons themselves, such as increases in passenger air travel during summer months when vacation rates tend to be higher.
How does the calendar affect the time series?
Calendar effects (trading days and holidays) often introduce additional movement in the time-series, and data outliers may disrupt movement altogether. Both calendar effects and data outliers make it difficult to uncover regular seasonal movement.
How are seasonally adjusted data used in statistics?
Statisticians use the process of seasonal-adjustment to uncover trends in data. Monthly data, for instance, are influenced by the number of days and the number of weekends in a month as well as by the timing of holidays and seasonal activity.