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Distinguishing chaos from random fractal sequences by the comparison of forward and backward predictions: utilization of the difference in time reversal symmetry of time series

机译:通过比较前向和后向预测来区分随机分形序列中的混沌:利用时间序列的时间反转对称性差异

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The authors propose a method for distinguishing chaos from random fractal sequences which have been difficult to discriminate from chaos. In the proposed method, the time series is predicted both in the forward direction and in the backward direction, and the accuracy of the two types of predictions is compared. They show, considering the time reversal symmetry of time series, that if the time series is chaotic and originates from a dissipative dynamical system, the accuracy is in general better for the forward prediction than for the backward prediction, whereas the accuracy is the same if the time series is a random fractal sequence. The method is also applicable to distinguishing between chaos and stationary noise. It is possible to give a quantitative evaluation of the distinction without a large amount of data or calculation.
机译:作者提出了一种区分混沌和随机分形序列的方法,这些分形序列很难与混沌区分开。在所提出的方法中,在正向和反向上都预测了时间序列,并且比较了两种类型的预测的准确性。他们表明,考虑到时间序列的时间反转对称性,如果时间序列是混沌的并且起源于耗散动力系统,则对于前向预测,其精度通常比向后预测的精度要好,而如果对时间序列的反向精度是相同的,则该精度是相同的。时间序列是一个随机的分形序列。该方法还适用于区分混沌噪声和平稳噪声。无需大量数据或计算就可以对区别进行定量评估。

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