Yu Dejin, Lu Weiping, Harrison Robert G.
Department of Physics, Heriot-Watt University Riccarton, Edinburgh EH14 4AS, United Kingdom.
Chaos. 1999 Dec;9(4):865-870. doi: 10.1063/1.166458.
Nonlinear time series analysis is becoming an ever more powerful tool to explore complex phenomena and uncover underlying patterns from irregular data recorded from experiments. However, the existence of dynamical nonstationarity in time series data causes many results of such analysis to be questionable and inconclusive. It is increasingly recognized that detecting dynamical nonstationarity is a crucial precursor to data analysis. In this paper, we present a test procedure to detect dynamical nonstationarity by directly inspecting the dependence of nonlinear statistical distributions on absolute time along a trajectory in phase space. We test this method using a broad range of data, chaotic, stochastic and power-law noise, both computer-generated and observed, and show that it provides a reliable test method in analyzing experimental data. (c) 1999 American Institute of Physics.
非线性时间序列分析正日益成为一种强大的工具，用于探索复杂现象，并从实验记录的不规则数据中揭示潜在模式。然而，时间序列数据中动态非平稳性的存在使得此类分析的许多结果存在疑问且无定论。人们越来越认识到，检测动态非平稳性是数据分析的关键前提。在本文中，我们提出了一种测试程序，通过直接检查非线性统计分布对相空间中沿轨迹的绝对时间的依赖性来检测动态非平稳性。我们使用广泛的数据，包括混沌、随机和幂律噪声，既有计算机生成的也有观测到的，来测试这种方法，并表明它为分析实验数据提供了一种可靠的测试方法。(c) 1999美国物理研究所。
