1346.0.55.003 - Interpreting Time Series: Are you being misled by the Seasons, 2012
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 11/12/2012 First Issue
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WHY SEASONALLY ADJUST?
Figure 1: Example Seasonal Pattern and Trend
SOME CAUSES OF SEASONAL PATTERNS
Seasonal patterns are caused by regularly repeating cycles in the real world. For example:
WHAT IF WE REMOVE SEASONAL PATTERNS?
Removing seasonal patterns can help you understand what has been happening to your data. This is because there is more to your data than just seasonal patterns. There is also some sort of underlying direction, known as the trend. Once you know the trend and the seasonal pattern everything left over is known as the residual noise or irregular part of the series.
The process of estimating and removing seasonal patterns is known as seasonal adjustment.
TELL ME MORE ABOUT THE TREND
The trend is the underlying direction of the series - "what’s normal". It smooths out most of the noise and short-term effects present in the seasonally adjusted. If the noise is strong then the seasonally adjusted data will be quite volatile, but the trend will give you a much better idea of the long term behaviour of the data.
The trend is estimated by smoothing noise out of the seasonally adjusted series.
WHY IS MY DATA NOISY?
Noisy, or volatile, data is caused by a number of factors:
Figure 2: Example Residual Series
If your data is very noisy or has weak seasonal patterns it may be difficult to obtain reliable seasonally adjusted estimates.
Sometimes once-off, irregular events will have an impact on your data. For example, a strike by pilots would lead to a sudden reduction in air travel. Irregular events can affect our ability to identify seasonal patterns, leading to potentially misleading seasonally adjusted estimates.
Irregular events must be corrected to obtain reliable estimates of the seasonal pattern.
Figure 3: Example Irregular Event
The impact of irregular events and noise can be seen in the seasonally adjusted but are removed from the trend.
Seasonal patterns, irregular events and residual noise can all make it difficult to understand what has been happening to our data.
Seasonal adjustment is a process for separating these influences. Seasonally adjusted and trend estimates allow us to highlight underlying characteristics of data.
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