Analysis of Time Series Structure SSA and Related Techniques

By Andrew L. Rukhin. Technometrics, 2002

875 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/290232680a375a9cb394e10053b773e282f5e03b

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In Analytical Chemistry

In Management Science and Operations Research

In Signal Processing

Citations in the corpus

Stochastic subspace identification for output‐only modal analysis: application to super high‐rise tower under abnormal loading condition (2013, methodology): “RECURSIVE SINGULAR SPECTRUM ANALYSIS Singular spectrum analysis is a novel nonparametric technique used in the analysis of time series based on multivariate statistics [16, 17].”
Separability and window length in singular spectrum analysis (2011, background): “Let X be the trajectory matrix defined as (1) and X = S+N = S̃+Ñ where, S = ∑ri=1 λi U i V T i , N = ∑Lj=r+1 √λ j U j V j , and matrices S̃ and Ñ are Hankelized matrices of S and N, respectively.”
TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise (2014, methodology): “In the Reconstruction Step, each group YIp is used to reconstruct a time series ỹp with the length M by a so-called diagonal averaging procedure [23].”
The "Caterpillar"-SSA method for analysis of time series with missing values (2007, methodology): “…signal processing, dynamic system analysis, stationary series analysis, signal detection in the presence of red noise, analysis of series governed by linear recurrent formulas, Principle Component Analysis, and the others (see Elsner and Tsonis (1996) and Golyandina et al (2001) for references).”
Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir (2014, background): “The main purpose of SSA is to decompose the original series into a sum of series, so that each component in this sum can be identified as either a trend, periodic, quasiperiodic or noise component (Golyandina et al. 2001).”