|SSA (Singular Spectrum Analysis) is a powerful statistical technique that provides a complete multi-wave representation of the market. It breaks price into "modes of variability" that capture the variance at any time frame. Like other transformations, SSA is designed to enhance certain aspects of a data set; in Fourier decomposition it is the frequencies whereas in SSA it is the variance.
Viewing cycles in terms of variance represents a radical departure from spectral analysis; it allows us to perform the transform from the data itself without making any assumption whatsoever on the way to proceed.
Fourier methods assume that future data consist of a periodic repetition of past data in the form of pure sinusoids. Wavelets offer yet another way of analyzing price series. They operate with a fixed set of filters of predetermined shape. There is usually little physical evidence in markets for such assumptions.
We find it most useful to extract cycles from the data itself, in which case the transform is ideally adapted to the kind of market that is being traded. Cycles are not forced to conform to some unsubstantiated mathematical representation therefore they retain a remarkable resemblance to the original series.
Following is a brief overview of the indicators in the CSSA add-on: