去趋势波动分析
系列(地层学)
相关性
数学
缩放比例
统计
期限(时间)
航程(航空)
自相关
统计物理学
相关函数(量子场论)
光谱密度
物理
古生物学
量子力学
几何学
材料科学
复合材料
生物
作者
Huanhuan Gong,Zuntao Fu
标识
DOI:10.1016/j.physa.2023.128997
摘要
Since the standard detrended fluctuation analysis (s-DFA) method depends on asymptotic power-law scaling assumption, it has been questioned on its ability in characterizing the correlation structure at the small scales, let alone discriminating the correlation structures of short time series. A modified DFA (m-DFA) with a novel defined fluctuation function is employed in this study to discriminate and characterize the distinct (short-termed or long-ranged) correlation structures in short time series. Detailed results show that the m-DFA is able not only to distinguish the short-termed, or long-ranged correlation, but also to well quantify the correlation strengths in both output from classical models and real-world series (for example, DTR variability over China) of data length as short as 2000. Moreover, m-DFA can work effectively in time series with strong correlation of even shorter length as 500. The correlation structures inferred by m-DFA in short intervals contribute greatly to better understanding of local and evolutionary correlation features of an underlying process, not limited to only global one from s-DFA.
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