In time series analysis, singular spectrum analysis (SSA) is a nonparametric spectral estimation method. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems, and signal processing —Wikipedia
The goal is to use this scientific tool for technical analysis. In order to understand SSA, we should focus on what it does rather than what it is. In this method, time series are decomposed and reconstructed.
With Singular Spectrum Analysis (SSA) we break down all the oscillations in a series into a few eigenvectors, which represent oscillation patterns. Furthermore, the significance of these patterns or eigenvalues is estimated.
“Eigen” is a German word, translating to “characteristic”. Eigenvectors are structure functions that effectively represent price modes of behavior. The eigenvalues measure the variance 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. As a whole, the ordered eigenvalues are referred to as the singular spectrum.
Note: SSA basically recalculates
The above screenshot shows that the RMA is ON with SSA. RMA adapts to changing market conditions. By giving greater weight to recent price data, it is more responsive to short-term price fluctuations and can help traders capture shorter-term trends. Therefore, it provides a more accurate representation of price movements by assigning greater weight to more recent data points. Hence, by using the RMA in conjunction with SSA, traders can enhance their decision-making process and potentially improve their trading outcomes.
However, it is important to note that like any technical indicator, the SSA & RMA is not infallible and should be used in conjunction with other (your own tried & tested) analysis tools and risk management strategies. It is also crucial to consider the context of the market and other factors that may influence price movements.
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