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摘要翻译：
Granger因果关系是时间序列数据因果关系推理的一种基本方法，在社会和生物科学中得到了广泛的应用。Granger因果关系的典型操作都有一个强有力的假设，即效应时间序列的每一个时间点都受到具有固定时间延迟的其他时间序列的组合影响。转移熵中也存在固定时滞的假设，它被认为是格兰杰因果关系的非线性版本。然而，固定时滞的假设在许多应用中并不成立，如集体行为、金融市场和许多自然现象。为了解决这个问题，我们发展了变滞后Granger因果关系和变滞后转移熵，这是Granger因果关系和转移熵的推广，放松了固定时滞的假设，允许原因对任意时滞的影响。此外，我们还提出了变量滞后Granger因果关系和转移熵关系的推断方法。在我们的方法中，我们利用动态时间规整(DTW)的最优规整路径来推断变量-滞后因果关系。我们在一个研究协调集体行为和其他现实世界随意推理数据集的应用中演示了我们的方法，并表明我们提出的方法在模拟和现实世界数据集上都比现有的几种方法表现得更好。我们的方法可以应用于时间序列分析的任何领域。本工作的软件可在R-CRAN包中获得：vltimecausality。
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英文标题：
《Variable-lag Granger Causality and Transfer Entropy for Time Series
Analysis》
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作者：
Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf
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最新提交年份：
2020
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Physics 物理学
二级分类：Data Analysis, Statistics and Probability 数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop Variable-lag Granger causality and Variable-lag Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allow causes to influence effects with arbitrary time delays. In addition, we propose methods for inferring both variable-lag Granger causality and Transfer Entropy relations. In our approaches, we utilize an optimal warping path of Dynamic Time Warping (DTW) to infer variable-lag causal relations. We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approaches can be applied in any domain of time series analysis. The software of this work is available in the R-CRAN package: VLTimeCausality.
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PDF链接：
https://arxiv.org/pdf/2002.00208
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