Singular spectrum analysis based on the perturbation theory
Singular spectrum analysis based on the perturbation theory
复制标题
DOI：
10.1016/j.nonrwa.2011.03.020
复制
发表时间：
2011-10-01
影响因子：
2
通讯作者：
Zhigljavsky, Anatoly
中科院分区：
文献类型：
--
作者：
Hassani, Hossein;Xu, Zhengyuan;Zhigljavsky, Anatoly
奇异谱分析（SSA）在不同的应用中得到了应用。众所周知，来自各种来源的扰动会严重降低方法和技术的性能。本文考虑了基于摄动理论的SSA技术，并检验了其在噪声序列重构和预测方面的性能。我们还考虑了该技术对不同窗长、噪声级和序列长度的敏感性。为了涵盖广泛的应用范围，采用了从动态到混沌的各种模拟序列来验证所提出的算法。然后，我们使用两个真实的知名系列来评估该技术的性能，即美国每月的意外死亡人数和几个股票市场指数的每日收盘价。结果与Box-Jenkins SARIMA模型、ARAR算法、GARCH模型和Holt-Winter算法等经典方法进行了比较。(c) 2011 Elsevier Ltd.版权所有。
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.