Singular spectrum analysis is a linear multivariate method for the analysis of time series data, based on principal component analysis of an augmented data set consisting of the original time series data and lagged copies of the data. It can be used to decompose the time series into a set of component time series, each of which could be investigated individually to gain a better understanding o...

Using multi-scale ideas from wavelet analysis, we extend singular-spectrum analysis (SSA) to the study of nonstationary time series, including the case where intermittency gives rise to the divergence of their variance. The wavelet transform resembles a local Fourier transform within a finite moving window whose widthW , proportional to the major period of interest, is varied to explore a broad...

1 Robust Singular Spectrum Analysis for Erratic Noise Attenuation Ke Chen*, University of Alberta, Edmonton, Canada [email protected] and Mauricio D. Sacchi, University of Alberta, Edmonton, Canada [email protected] Summary The Singular Spectrum Analysis (SSA) method, also known as Cadzow filtering, adopts the truncated singular value decomposition (TSVD) or fast approximations to TSVD for rank...

The low dimensionality of magnetospheric activity indicated by previous phase space reconstructions using the AE and AL data suffer from the limitations of these t chniques. In this paper the singular spectrum analysis is used to study the global magnetospheric dynamics using the AE data and it yields a correlation dimension m2.5, thus confirming the low dimensionality published earlier. Furthe...

Singular Spectrum Analysis (SSA) is a powerful data decomposition/recompostion technique that can be used to reduce the noise in time series. Compared to existing solutions aiming at similar purposes, such as frequency-based filtering, it benefits from easier-to-exploit intuitions, applicability in contexts where low sampling rates make standard frequency analyses challenging, and the (theoreti...

Singular spectrum analysis is developed as a nonparametric spectral decomposition of a time series. It can be easily extended to the decomposition of multidimensional lattice-like data through the filtering interpretation. In this viewpoint, the singular spectrum analysis can be understood as the adaptive and optimal generation of the filters and their two-step point-symmetric operation to the ...

Watermarking is to embed a structure called a watermark into the target data such as images. The watermark can be used, for example, in order to secure the copyright and detect tampering. This paper presents a new robust watermarking method that adds a watermark into a 3D polygonal mesh in the spectral domain. In this algorithm, a shape of a 3D polygonal model is regarded as a sequence of verti...

• 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 is a natural generalization of principal component methods for time series data. In this paper we propose an imputation method to be used with singular spectrum-based techniques which is based on a weighted combination of the forecasts and hindcasts yield by the recurrent forecast method. Despite its ease of implementation, the obtained results suggest an overall good...

Each D A microarray experiment generates a large amount of gene expression profiles and it remains a challenge for biologists to robustly identify periodic gene expression profiles with certain noise level in the data. In this paper, we propose a new scheme with noise filtering technique to analyze the periodicity of gene expression base on singular value decomposition (SVD), singular spectrum ...