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 …

Singular spectrum analysis is a powerful and widely used non-parametric method to analyse
and forecast time series. Although singular spectrum analysis has proven to outperform …

In all fields of quantitative research, analysing data with missing values is an excruciating
challenge. It should be no surprise that given the fragmentary nature of fossil records, the …

Abstract Singular Spectrum Analysis (SSA) is a powerful non-parametric framework to
analysis and enhancement of time-series. SSA may be capable of decomposing a time …

In Chap. 2, SSA is normally considered as a model-free technique. The main body of Chap.
2 is devoted to careful description of Basic SSA, its main capabilities, choice of parameters …

The connection between Singular Spectrum Analysis (SSA) decomposition and short-term
market movements is investigated. Since SSA is a non-parametric approach, suitable to …

Missing values in time series data is a well-known and important problem which many
researchers have studied extensively in various fields. In this paper, a new nonparametric …

Singular spectrum analysis (SSA) is a nonparametric method for separating time series data
into a sum of small numbers of interpretable components (signal+ noise). One of the steps of …

Background: Heart rate variability (HRV) is derived from the series of RR intervals extracted
from an electrocardiographic (ECG) measurement. Ideally all components of the RR series …

In the rapidly advancing field of autonomous systems, real-time operation and monitoring in
non-stationary environments frequently relies on analysis (filtering/prediction) of short …