earticle

영어

The significance of treating astronomical phenomenon as a chaotic system instead of a stochastic system for a better understanding of the underlying has been grown considerably in recent years. Unfortunately, the most important disadvantage of these techniques is the dependence on a single approach for identifying the chaotic nature and the variables involved. In this paper, an attempt is proposed to detect chaotic nature of astronomical time series using various nonlinear analysis techniques and prediction is also done to quantify the uncertainty involved. Mutual information function and Cao algorithm are used to determine the time delay and embedding dimension for the phase space reconstruction. The probability time and secular persistence or memory are calculated using maximal Lyapunov exponent and rescaled range analysis. Our analysis results indicate that dynamical behavior of astronomical process should be controlled by a low-dimensional chaoticattractor, its chaotic motion can be considered as a long-term persistence on large scales. Moreover, the nonlinear prediction method employed support the short- and mid-term predictability nature of astronomical time series. We can arrive at a conclusion that the reasonably good predictions obtained using a chaos theory is helpful for modeling and understanding the underlying dynamics of the complex astronomical system.