Seasonal Trend: When data changes or fluctuates at given intervals, have a fixed or regular period of time between peaks. eg, Temp is high every summer.
When a seasonal trend is present, it is difficult to determine if there are any other trends within the data, eg upwards trend or downwards trend.
To remove the effect of the peaks during seasons, we can seasonally adjust (deseasonalise) the original time series to remove the seasonal variation, therefore exposing any underlying trends.
Example:
Temperatures have been measured each season for the years 2004-2008. It is difficult to recognise any trends, other than a seasonal trend. Deseasonalise the data and comment on the result.
The basic formula to desonalise data is:Deasonalised figure = original figure / seasonal index
If you are given only the original data, you will need to work out the seasonal index first.
So this is the basic process.
1. Find the yearly average.
eg. (Summer-04 + Autumn-04 + Winter-04 + Spring-04) / 42. Divide each number in the original time series by its yearly average found in step 1.
eg. Summer-04 / 2004 Average3. Find the seasonal averages (also known as seasonal indices)
eg. (Summer-04 + Summer-05 + Summer-06 + Summer-07 + Summer-08) / 54. Divide each of the numbers from the original data by the corresponding seasonal index
eg. Summer-04/Summer Seasonal IndexYayy you will now have the new seasonally adjusted or deseasonalised time series

5. Now, you can graph the new seasonal adjusted data against alone or with the original data if you want to compare. Most of the time, some or most of the seasonal variation will be removed.
You might then be able to see a slight upward trend, or a downwards trend, or maybe no trend at all
