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How is the trend cycle estimated in classical decomposition?
The trend-cycle estimate tends to over-smooth rapid rises and falls in the data (as seen in the above example). Classical decomposition methods assume that the seasonal component repeats from year to year. For many series, this is a reasonable assumption, but for some longer series it is not.
What are the two types of classical decomposition?
There are two forms of classical decomposition: an additive decomposition and a multiplicative decomposition. These are described below for a time series with seasonal period m m (e.g., m = 4 m = 4 for quarterly data, m= 12 m = 12 for monthly data, m = 7 m = 7 for daily data with a weekly pattern).
How is seasonal decomposition used in time series?
Classical Seasonal Decomposition One use of a creating a smoothed trend line in this fashion is to perform seasonal decomposition, to break out the original data into components for the trend, cyclical deviations from this trend and then whatever residuals are left over. How do we go from a smoothed line to the full decomposition?
Which is a reasonable assumption for classical decomposition?
Classical decomposition methods assume that the seasonal component repeats from year to year. For many series, this is a reasonable assumption, but for some longer series it is not. For example, electricity demand patterns have changed over time as air conditioning has become more widespread.
Are there still any problems with classical decomposition?
While classical decomposition is still widely used, it is not recommended, as there are now several much better methods. Some of the problems with classical decomposition are summarised below. The estimate of the trend-cycle is unavailable for the first few and last few observations.
Is the seasonal component constant in classical decomposition?
These are described below for a time series with seasonal period m m (e.g., m = 4 m = 4 for quarterly data, m= 12 m = 12 for monthly data, m = 7 m = 7 for daily data with a weekly pattern). In classical decomposition, we assume that the seasonal component is constant from year to year.
Is there an estimate of the trend cycle?
The estimate of the trend-cycle is unavailable for the first few and last few observations. For example, if m = 12 m = 12, there is no trend-cycle estimate for the first six or the last six observations. Consequently, there is also no estimate of the remainder component for the same time periods.