The high impact of air quality on environmental and human health justifies the increasing
research activity regarding its measurement, modelling, forecasting and anomaly detection …

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 …

In this paper, we present a method of utilizing spatial information, usually intrinsic in spatial
data sets, to improve the quality of temporal predictions within the framework of singular …

The decomposition of multichannel signals into their underlying components is a problem of
key interest in many disciplines. It involves the analysis of nonlinear and nonstationary time …

The core intention of this chapter is providing the reader with more theoretical information
regarding technical background of the Singular Spectrum Analysis (SSA) approach so that …

This chapter is devoted to an approach of extracting periodic or quasi-periodic components
from a random signal. Singular Spectrum Analysis (SSA) is not, in a strict sense, a simple …

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 …

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 the popular tool for analysing and forecasting time
series. SSA can be used for parametric estimation, forecasting and gap filling amongst many …

Singular spectrum analysis (SSA) is a non-parametric adaptive technique used for time
series analysis. It allows solving various problems related to time series without the need to …