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arXiv · 2005.00487

Time-Spatial Serials Differences' Probability Distribution of Natural Dynamical Systems

Wei Ping Cheng·Zhi Hong Zhang·Pu Wang
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Abstract

The normal distribution is used as a unified probability distribution, however, our researcher found that it is not good agreed with the real-life dynamical system's data. We collected and analyzed representative naturally occurring data series (e.g., the earth environment, sunspots, brain waves, electrocardiograms, some cases are classic chaos systems and social activities). It is found that the probability density functions (PDFs) of first or higher order differences for these datasets are consistently fat-tailed bell-shaped curves, and their associated cumulative distribution functions (CDFs) are consistently S-shaped when compared to the near-straight line of the normal distribution CDF. It is proved that this profile is not because of numerical or measure error, and the t-distribution is a good approximation. This kind of PDF/CDF is a universal phenomenon for independent time and space series data, which will make researchers to reconsider some hypotheses about stochastic dynamical models such as Wiener process, and therefore merits investigation.

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Wei Ping Cheng, Zhi Hong Zhang, Pu Wang. 2020-04-30. Time-Spatial Serials Differences' Probability Distribution of Natural Dynamical Systems. https://arxiv.org/abs/2005.00487

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