A new approach for selecting the number of the eigenvalues in singular spectrum analysis

Singular spectrum analysis (SSA) is a reliable technique for separating an arbitrary signal from a noisy time series (signal+noise). The SSA technique is based upon two main selections: window length, L, and the number of the eigenvalues, r. These values play an important role for the reconstruction stage. In this paper, we introduce a new approach for selecting the optimal value of r, which is based on the distribution of the eigenvalues of a scaled Hankel matrix. The proposed approach is applied to a number of simulated and real data with different structures. The results indicate that the proposed approach has potential in selecting the value of r for signal extraction.