变的非对称分布的变
A A Rey1, A C Frery2, J Gambini3
1CIDIA, Universidad Nacional de Hurlingham, Villa Santos Tesei 1688, Argentine Republic and CPSI, Universidad Tecnológica Nacional Regional Buenos Aires, Ciudad Autónoma de Buenos Aires 1179, Argentine Republic.
这项研究分析了时间序列中的 permutation entropy,解决了模式相关性. 它将衍生分布与多项假设进行比较,以更好地理解顺序模式动态.
科学领域:
- 复杂的系统复杂的系统.
- 时间序列分析时间序列分析
- 信息理论 信息理论
背景情况:
- 顺序模式为时间序列数据提供了强大的象征性转换.
- 从顺序模式衍生出的和统计复杂性缺乏充分的统计理解.
- 顺序模式的非独立性挑战了标准的多项式分布假设.
研究的目的:
- 为了计算参数变量的非对称分布,考虑顺序模式相关性.
- 为了比较这个衍生分布与使用多项式定律计算的.
- 增强对顺序模式统计属性和时间序列动态的理解.
主要方法:
- 从时间序列数据中构建顺序图的顺序图.
- 计算变量和比较它的非对称分布与多项.
- 开发时间序列的模拟算法,具有指定的模式直方图和过渡概率.
主要成果:
- 计算了变量的非对称分布,并考虑了模式相关性.
- 一项比较分析揭示了衍生分布和基于多项式的之间的差异.
- 模拟算法已成功开发,用于生成具有受控模式属性的时间序列.
结论:
- 顺序模式的非独立性需要超越简单的多项式假设的专业方法.
- 这项研究为时间序列中的 permutation entropy提供了一个更准确的统计框架.
- 这些发现有助于更深入地了解复杂系统中的符号动态和信息措施.
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