说明：  Tensor based singular spectrum analysis (SSA) has been in- troduced as an extension of traditional singular value decom- position (SVD) based SSA. In the SSA decomposition stage PARAFAC tensor factorization has been employed. Using tensor factorization methods enable SSA to perform much better in nonstationary and underdetermined cases. The re- sults of applying the proposed method to both synthetic and real data show that this system outperforms the original SSA, when used for single channel data decomposition in nonsta- tionary and underdetermined source separation