面向非平稳时间序列的因果关系发现算法
Causal Discovery Algorithm for Non-stationary Time Series
周嘉颖 1周跃进1
作者信息
- 1. 安徽理工大学数学与大数据学院,安徽 淮南 232001
- 折叠
摘要
针对传统因果关系算法不能分析非平稳时间序列和可变时滞时间序列数据因果关系的问题,本文提出一种基于分段聚合近似可变时滞转移熵(PAAVL-TE)的因果关系算法.利用分段聚合近似法对时间序列进行转换,提取时间序列的特征信息,运用动态时间弯曲距离寻找相似程度最高的时间序列计算可变时滞时间序列的转移熵,实现了非平稳时间序列的因果分析.通过计算机仿真模拟实验将提出的算法与存在的算法相比较,证实算法有效性.将该算法用于北京市昌平区PM2.5浓度和气象数据分析,表明本文算法具有广泛的应用性.
Abstract
To infer the causality of non-stationary time series accurately,a method of causal discovery algorithm based on piecewise ag-gregate variable-lag transfer entropy was proposed to overcome shortcomings of traditional methods of analyzing the causality of non-sta-tionary and variable-lag time series.Firstly,we used piecewise aggregate approximation to transform the time series and extract features.Then,dynamic time warping was used to infer variable-lag transfer entropy,which implemented causal relations between non-stationary time series.The effectiveness of the proposed method is verified by the results of simulations and comparison with existing methods.We successfully applied this method to the concentration of PM2.5 and meteorological time series in Changping,Beijing,which shows that the algorithm is widely applicable.
关键词
非平稳时间序列/分段聚合近似/转移熵/可变时滞/因果关系Key words
non-stationary time series/piecewise aggregate approximation/transfer entropy/variable-lag/causal relations引用本文复制引用
基金项目
深部煤矿采动响应与灾害防控国家重点实验室基金资助项目(SKLMRDPC22KF03)
出版年
2024
NETL
NSTL
万方数据