方差的n步滑动递推公式及其实现

Lang Yu, Gang He, Ahmad Khwaja Mutahir
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引用次数: 3

摘要

一个随机变量的离散程度可以用方差来描述,方差反映了随机变量与其均值的距离。然而,传统方差计算算法的时间复杂度为O(n),这是对所有样本进行充分计算的结果。当样本数量增加或在高速信号处理的情况下,时间复杂度为O(n)的算法将花费大量的时间,这可能会导致整个系统的性能下降。针对时间复杂度为0(1)的时变数据序列,提出了一种新的多步递推方差计算算法。对该算法进行了数值模拟和实验,结果表明,所提出的多步递推算法可以有效地减少计算时间,从而显著提高时变数据的方差计算效率,对耗时数据分析或高速信号处理具有潜在价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
N-Step Sliding Recursion Formula of Variance and Its Implementation
The degree of dispersion of a random variable can be described by the variance, which reflects the distance of the random variable from its mean. However, the time complexity of the traditional variance calculation algorithm is O(n), which results from full calculation of all samples. When the number of samples increases or on the occasion of high speed signal processing, algorithms with O(n) time complexity will cost huge amount of time and that may results in performance degradation of the whole system. A novel multi-step recursive algorithm for variance calculation of the time-varying data series with O(1) time complexity (constant time) is proposed in this paper. Numerical simulation and experiments of the algorithm is presented and the results demonstrate that the proposed multi-step recursive algorithm can effectively decrease computing time and hence significantly improve the variance calculation efficiency for time-varying data, which demonstrates the potential value for time-consumption data analysis or high speed signal processing.
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