SSA means Singular Spectrum Analysis.

Singular Spectrum Analysis (SSA) is a method that takes all the variability in a series and breaks it into a few oscillation patterns that we refer to as eigenvectors. It also gives a measure of significance of these patterns or eigenvalues.

“Eigen” is a German word, which roughly translates to “characteristic”. Eigenvectors are structure functions that best represent the modes of behavior in price. The eigenvalues are a measure of the variance that these modes account for.

The eigenvectors can be ordered by eigenvalue, highest to lowest, so that the first few patterns retain most of the variance in the data. The ordered eigenvalues are referred to collectively as the Singular Spectrum.

SSA_Normalize uses the component that accounts for most of the variability in the data; it is the lower frequency component.

After that it generates a period of normalization that results into a moving average.

SSA is an universal oscillator, it can emulate every possible oscillator.