Seasonality in Data
Here is a good excerpt about seasonality.
"For analyzing general price trends in the economy, seasonally adjusted changes are usually preferred since they eliminate the effect of changes that normally occur at the same time and in about the same magnitude every year—such as price movements resulting from changing climatic conditions, production cycles, model changeovers, holidays, and sales." source
I am not an expert statistician, but hopefully the two examples I found below will help give you a high level understanding of seasonality. At a minimum, when you see footnotes in periodicals, like the one in a recent post, you may have a better understanding. Also, when analyzing data, it’s always a good rule to think about the data and if there is a seasonal effect involved. There could be drastically different results if you tried to forecast or model using data from a period of extreme seasonality.
Pre Adjustment for Seasonality
The seasonal effect is extremely visible in the example below.
Seasonally Adjusted
After the data is adjusted for seasonality (smoothed out) it is much easier to see the periods of decline.
Bureau of Transportation Statistics (source)
Related to Seasonality
X-12 ARIMA – A Census Bureau method for removing seasonal factors
BV4.1 Developed by the Federal Statistical Office of Germany, this software can adjust data for seasonality
Jon Peltier – Blog post about seasonality
Most Commented Posts
December 8th, 2008 at 10:32 am
I’d like to see an easy-to-implement version of that smoothing algorithm. It’s much more effective than a simple 12 month moving average.