Singular spectrum analysis (SSA) is a method of time-series analysis based on the singular value decomposition of an associated Hankel matrix. We present an approach to SSA using an effective and numerically stable high-degree polynomial approximation of a spectral projector, which also provides a means of time-series forecasting. Several numerical examples illustrating the algorithm are given.

The combined application of Singular Spectrum Analysis (SSA) and Cluster Analysis to the automatic smoothing of raw kinematic signals is an alternative to the use of traditional digital filtering and spline based methods. SSA is a non parametric technique that decomposes original time series into a number of additive time series each of which can be easily identified as being part of the noise ...

the record of human brain neural activities, namely electroencephalogram (eeg), is generally known as a non-stationary and nonlinear signal. in many applications, it is useful to divide the eegs into segments within which the signals can be considered stationary. combination of empirical mode decomposition (emd) and hilbert transform, called hilbert-huang transform (hht), is a new and powerful ...

General. Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. From the algorithmic point of view, SSA can be considered as a typical subspace-based method of signal processing. SSA aims at decomposing the original se...

In a referendum conducted in the United Kingdom (UK) on June 23, 2016, $51.6\%$ of the participants voted to leave the European Union (EU). The outcome of this referendum had major policy and financial impact for both UK and EU, and was seen as a surprise because the predictions consistently indicate that the ``Remain'''' would get a majority. In this paper, we investigate whether the outcome o...

• The paper presents a new method of trend extraction in the framework of the Singular Spectrum Analysis (SSA) approach. This method is easy to use, does not need specification of models of time series and trend, allows to extract trend in the presence of noise and oscillations and has only two parameters (besides basic SSA parameter called window length). One parameter manages scale of the ext...

Singular-spectrum analysis (SSA) is a powerful technique of time series analysis. SSA is based on a singular value decomposition of a ‘trajectory matrix’ obtained from the original time series with subsequent reconstruction of the series. A methodology of change-point detection in time series based on sequential application of the singular-spectrum analysis is proposed and studied. The underlyi...