SSA, Random Matrix Theory, and Noise-Reduced Correlations

This is the third paper in a series devoted to obtaining noise-reduced, stable correlations by smoothing time series using Singular Spectrum Analysis, or SSA. Here we show that the SSA-based correlations are superior in terms of noise reduction, employing a number of simple tests using Random Matrix Theory (RMT) constructs. In each case, the correlations obtained using SSA-smoothed time series are further from noise than are conventional correlations. “Noise” here is defined by a zero-correlation Wishart random matrix WRM composed of correlations between series filled with independent Gaussian random numbers