Novel Tensor-Based Singular Spectral Decomposition

Tensor-based signal decomposition method offers a promising avenue for signal decomposition of short, nonstationary and non-linear input signals. A novel Tensor-based Singular Spectrum Decomposition (TSSD) framework is presented that extends Singular Spectrum Decomposition (SSD) to tensors for univariate signals using two tensor decomposition techniques, namely, Multilinear Singular Value Decomposition (MLSVD) and Canonical Polyadic Decomposition (CPD). Results indicate improved performance under the influence of noise and in the presence of sizeable trends. Experiments on real-life tide and tsunami data also show the practical relevance of TSSD as a tool for exploratory signal analysis that helps unveil underlying system(s) in signals.