摘要
基于非线性时间序列分析方法———动力学相关因子指数,提出一种新的动力结构突变的检测方法———动力学指数分割算法.通过理想时间序列试验,验证了该方法检测动力结构突变的有效性,同时发现相对少量的尖峰噪声对该方法的影响较小,但连续分布的随机白噪声对其具有一定的影响,并与传统的滑动T检验法和Yamamoto法进行比较,进而讨论它们各自的优缺点.
For a long time in the past, researches of time series were often based on their external characters and used linear and statistical methods. However, most actual systems arc nonlinear, nonstationary and complicated, which increased the diffculties in treating them. The research of abrupt change is one of most important research aspects of nonlinear time series, for which the traditional method based on the external characters of data and using linear process lacks enough physical foundation, and has obvious limitations. How to find out the essence of complicated systems from time series, in other words, to check the abrupt change in dynamical structure of actual data series is a really important problem pending solution. In the present paper, we present a new method——the dynamical correlation exponent segmentation algorithm for checking dynamical abrupt change based on the dynamical lag correlation exponent. The validity of this method is verified by constructing an ideal time series and put it to test. It was found that a few noise spikes have little influence, but continuously distributed white noise has some influence to this new method. Comparison with conventional t-test and Yamamoto method was made to show the relative merits of the methods.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2006年第6期3180-3187,共8页
Acta Physica Sinica
基金
国家重点基础研究发展规划项目(批准号:2006CB400503)
国家自然科学基金(批准号:90411008和40325015)资助的课题~~
关键词
动力学相关因子指数
动力学指数分割算法
噪声
滑动T检验
Yamamoto法
dynamical lags correlation exponent, dynamical correlation exponent segmentation algorithm, noise, student＇s ttest, Yamamoto method
