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Singular spectrum analysis based on the perturbation theory

Hassani, Hossein, Xu, Zhengyuan and Zhigljavsky, Anatoly Alexandrovich 2011. Singular spectrum analysis based on the perturbation theory. Nonlinear Analysis: Real World Applications 12 (5) , pp. 2752-2766. 10.1016/j.nonrwa.2011.03.020

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Abstract

Singular Spectrum Analysis (SSA) has been exploited in different applications. It is well known that perturbations from various sources can seriously degrade the performance of the methods and techniques. In this paper, we consider the SSA technique based on the perturbation theory and examine its performance in both reconstructing and forecasting noisy series. We also consider the sensitivity of the technique to different window lengths, noise levels and series lengths. To cover a broad application range, various simulated series, from dynamic to chaotic, are used to verify the proposed algorithm. We then evaluate the performance of the technique using two real well-known series, namely, monthly accidental deaths in the USA, and the daily closing prices of several stock market indices. The results are compared with several classical methods namely, Box–Jenkins SARIMA models, the ARAR algorithm, GARCH model and the Holt–Winter algorithm.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Uncontrolled Keywords: Singular spectrum analysis; Perturbation theory; Reconstruction; Forecasting
Publisher: Elsevier
ISSN: 1468-1218
Last Modified: 04 Jun 2017 02:47
URI: http://orca.cf.ac.uk/id/eprint/12146

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