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. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘...

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...

Singular Spectrum Analysis (SSA) has been approved as a model-free technique to analyse time series. SSA can solve different problems such as decomposition into a sum of trend, periodicities, and noise, smoothing, and others. In this paper, we validate abilities of 2D-SSA (the extension of SSA to analyse two-dimensional scalar fields) to treat digital terrain models (DTMs). The study is exempli...

Extensions of singular spectrum analysis (SSA) for processing of non-rectangular images and time series with gaps are considered. A circular version is suggested, which allows application of the method to the data given on a circle or on a cylinder, e.g. cylindrical projection of a 3D ellipsoid. The constructed Shaped SSA method with planar or circular topology is able to produce low-rank appro...

We show that multivariate singular spectrum analysis (M-SSA) greatly helps study phase synchronization in a large system of coupled oscillators and in the presence of high observational noise levels. With no need for detailed knowledge of individual subsystems nor any a priori phase definition for each of them, we demonstrate that M-SSA can automatically identify multiple oscillatory modes and ...

This paper present a generalization of a data analysis technique called a singular spectrum analysis (SSA). The original SSA is a tool for analyzing one-dimensional data such as time series, whereas our generalization is suitable for multi-dimensional data such as 3D polygonal meshes. One of applications of the proposed generalization are also shown. The application of the generalized SSA is a ...

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA technique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are compared with those obtained using Box-Jenkins ...

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, we introduce the SSA technique based on the minimum variance estimator. We also consider the SSA technique based on the minimum variance and structured total least squares estimators in reconstructing and forecasting ti...

The neural network-based nonlinear singular spectrum analysis (NLSSA) is applied to the zonal winds in the 70-10 hPa region (roughly 20-30 km altitude) measured at near-equatorial stations during 1956-2000. The data are pre-filtered by the linear singular spectrum analysis (SSA), with the leading 8 SSA principal components (PCs) used as inputs for the NLSSA. The NLSSA fits a curve to the data i...

We estimated the common seasonal signal (annual oscillation) included in the Global Positioning System (GPS) vertical position time series by using Multichannel Singular Spectrum Analysis (MSSA). We employed time series from 24 International GNSS Service (IGS) stations located in Europe which contributed to the newest ITRF2014 (International Terrestrial Reference Frame). The MSSA method has an ...