论文标题
使用极端事件推断长期记忆
Inferring long memory using extreme events
论文作者
论文摘要
许多自然和物理过程都会显示长期记忆和极端事件。在这些系统中，测得的时间序列总是被噪声污染。由于极端事件与平均行为表现出很大的偏差，因此噪声不会影响极端事件，而是影响典型值。由于极端事件还携带有关全日制序列中相关性的信息，因此可以使用它们来推断后者的相关性能。在这项工作中，从给定的时间序列中，我们仅使用极端事件构建了三个修改时间序列。结果表明，原始时间序列和修改时间序列中的相关性是由从降解波动分析技术获得的指数衡量的。因此，仅凭其极端事件就可以推断出长期内存时间序列的相关指数。在几个经验时间序列中证明了这种方法。
Many natural and physical processes display long memory and extreme events. In these systems, the measured time series is invariably contaminated by noise. As the extreme events display large deviation from the mean behaviour, the noise does not affect the extreme events as much as it affects the typical values. Since the extreme events also carry the information about correlations in the full time series, they can be used to infer the correlation properties of the latter. In this work, from a given time series, we construct three modified time series using only the extreme events. It is shown that the correlations in the original time series and in the modified time series, as measured by the exponent obtained from detrended fluctuation analysis technique, are related to each other. Hence, the correlation exponents for a long memory time series can be inferred from its extreme events alone. This approach is demonstrated for several empirical time series.
