Zhang Yi, Wang Guan, Li Ziwen, Xie Mingjun, Celler Branko, Su Steven, Xu Peng, Yao Dezhong
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China.
Key Laboratory for Neuro Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Front Neuroinform. 2022 Jun 16;16:851645. doi: 10.3389/fninf.2022.851645. eCollection 2022.
Causality inference has arrested much attention in academic studies. Currently, multiple methods such as Granger causality, Convergent Cross Mapping (CCM), and Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) are introduced to solve the problem. Motivated by the researchers who uploaded the open-source code for causality inference, we hereby present the Matlab code of NA-MEMD Causal Decomposition to help users implement the algorithm in multiple scenarios. The code is developed on Matlab2020 and is mainly divided into three subfunctions: _, , and __. _ is called in the main function to generate the matrix of Intrinsic Mode Functions (IMFs) and can display the average frequency and phase difference of IMFs of the same order in a matrix which can be used for the selection of the main Intrinsic Causal Component (ICC) and ICCs set. __ is called to perform causal redecomposition after removing the main ICC from the original time series and output the result of NA-MEMD Causal Decomposition. The performance of the code is evaluated from the perspective of executing time, robustness, and validity. With the data amount enlarging, the executing time increases linearly with it and the value of causal strength oscillates in an ideally small interval which represents the relatively high robustness of the code. The validity is verified based on the open-access predator-prey data (wolf-moose bivariate time series from Isle Royale National Park in Michigan, USA) and our result is aligned with that of Causal Decomposition.
因果关系推断在学术研究中备受关注。目前，已引入多种方法来解决该问题，如格兰杰因果关系、收敛交叉映射（CCM）和噪声辅助多元经验模态分解（NA - MEMD）。受上传因果关系推断开源代码的研究人员启发，我们在此展示NA - MEMD因果分解的Matlab代码，以帮助用户在多种场景中实现该算法。该代码基于Matlab2020开发，主要分为三个子函数：、和。_在主函数中被调用以生成本征模态函数（IMF）矩阵，并且可以在一个矩阵中显示相同阶次IMF的平均频率和相位差，该矩阵可用于选择主要本征因果分量（ICC）和ICC集。__在从原始时间序列中去除主要ICC后被调用以执行因果再分解，并输出NA - MEMD因果分解的结果。从执行时间、稳健性和有效性的角度对代码性能进行评估。随着数据量的增加，执行时间与之线性增加，因果强度值在一个理想的小间隔内振荡，这表明代码具有较高的稳健性。基于开放获取的捕食者 - 猎物数据（来自美国密歇根州皇家岛国家公园的狼 - 驼鹿双变量时间序列）验证了有效性，我们的结果与因果分解的结果一致。
