Journal
NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS
Volume 12, Issue 5, Pages 2752-2766Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.nonrwa.2011.03.020
Keywords
Singular spectrum analysis; Perturbation theory; Reconstruction; Forecasting
Categories
Ask authors/readers for more resources
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. (c) 2011 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available