Singular spectrum analysis (SSA) is a signal decomposition technique that aims at
expanding signals into interpretable and physically meaningful components (eg, sinusoids …

This study introduces singular spectrum decomposition (SSD), a new adaptive method for
decomposing nonlinear and nonstationary time series in narrow-banded components. The …

Signal decomposition (SD) approaches aim to decompose non-stationary signals into their
constituent amplitude-and frequency-modulated components. This represents an important …

Singular spectrum analysis (SSA) as a nonparametric tool for decomposition of an observed
time series into sum of interpretable components such as trend, oscillations and noise is …

Singular spectrum analysis (SSA) is a powerful approach to separate sources from the
mixed signal. There is an important factor when SSA is used for extracting each source …

After observing a noisy time series or signal, it is common practice to try to separate the
noise from the observed measurements. Singular value decomposition based methods are …

As a very recent technique for time-series analysis, singular spectrum analysis (SSA) has
been applied in many diverse areas, where an original 1-D signal can be decomposed into …

Singular spectrum analysis (SSA), starting from the second half of the 20th century, has
been a rapidly developing method of time series analysis. Since it can be called principal …

Sometimes, it is of interest to single out the fluctuations associated to a given frequency. We
propose a new variant of SSA, Circulant SSA (CiSSA), that allows to extract the signal …

In this introduction we briefly describe the methodology of the Singular Spectrum Analysis
(SSA), some versions and extensions of the basic version of SSA as well as connections …