论文标题
压缩复杂性具有序数模式，用于不规则采样的时间序列中的强大因果推断
Compression-Complexity with Ordinal Patterns for Robust Causal Inference in Irregularly-Sampled Time Series
论文作者
论文摘要
效果的区分是一项科学挑战，可以抵抗数学，统计，信息理论和计算机科学的解决方案。压缩复杂性因果关系（CCC）是最近提出的有关因果关系的介入措施，灵感来自维纳·葛兰格（Wiener-Granger）的想法。它基于原因变量，基于效应变量的动态压缩复合度（或压缩性）的变化估计因果关系。 CCC对给定数据的假设最少，并且对不规则采样，缺失数据和有限长度效应具有鲁棒性。但是，它仅适用于一维时间序列。我们提出了一个顺序的模式象征方案，将多维模式编码为一维符号序列，从而引入置换CCC（PCCC），该序列保留了原始CCC的所有优势，并可以将其应用于具有潜在隐藏变量的多维系统中的数据。 PCCC在数值模拟上进行了测试，并应用于以不规则和不确定的采样和有限数量的样品为特征的古气候数据。
Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of causality, inspired by Wiener-Granger's idea. It estimates causality based on change in dynamical compression-complexity (or compressibility) of the effect variable, given the cause variable. CCC works with minimal assumptions on given data and is robust to irregular-sampling, missing-data and finite-length effects. However, it only works for one-dimensional time series. We propose an ordinal pattern symbolization scheme to encode multidimensional patterns into one-dimensional symbolic sequences, and thus introduce the Permutation CCC (PCCC), which retains all advantages of the original CCC and can be applied to data from multidimensional systems with potentially hidden variables. PCCC is tested on numerical simulations and applied to paleoclimate data characterized by irregular and uncertain sampling and limited numbers of samples.
