Chaos identification through the auto-correlation function indicator (ACFI)

V. Carruba, S. Aljbaae, R. C. Domingos, M. Huaman, W. Barletta
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引用次数: 3

Abstract

Abstract Close encounters or resonances overlaps can create chaotic motion in small bodies in the Solar System. Approaches that measure the separation rate of trajectories that start infinitesimally near, or changes in the frequency power spectrum of time series, among others, can discover chaotic motion. In this paper, we introduce the ACF index (ACFI), which is based on the auto-correlation function of time series. Auto-correlation coefficients measure the correlation of a time-series with a lagged duplicate of itself. By counting the number of auto-correlation coefficients that are larger than 5% after a certain amount of time has passed, we can assess how the time series auto-correlates with each other. This allows for the detection of chaotic time-series characterized by low ACFI values.
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基于自相关函数指示器的混沌识别
近距离接触或共振重叠会在太阳系的小天体中产生混沌运动。测量在无限小附近开始的轨迹的分离率的方法,或者时间序列的频率功率谱的变化,等等,可以发现混沌运动。本文介绍了基于时间序列自相关函数的ACF指数(ACFI)。自相关系数测量时间序列与自身滞后副本的相关性。通过计算一定时间后大于5%的自相关系数的数量,我们可以评估时间序列如何相互自相关。这允许检测以低ACFI值为特征的混沌时间序列。
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