Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing casual discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series casual discovery, and event sequence casual discovery. However, most previous surveys are only focused on the time series casual discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data casual discovery.

翻译：时序数据，即对复杂系统进行按时间顺序观测所得的数据，是一种典型的数据结构，可广泛产生于工业、医学和金融等多个领域。分析此类数据对各类应用极具价值。因此，过去几十年间，人们提出了多种时序数据分析任务，例如分类、聚类和预测。其中，因果发现（即从时序数据中学习因果关系）被认为是一项有趣且关键的任务，并吸引了大量研究关注。现有的因果发现工作可根据时序数据是否经过校准分为两个高度相关的类别：多变量时间序列因果发现和事件序列因果发现。然而，大多数以往的综述仅聚焦于时间序列因果发现，而忽略了第二类。在本文中，我们明确了这两个类别之间的关联，并对现有解决方案进行了系统性的概述。此外，我们还提供了公共数据集、评估指标以及时序数据因果发现的新视角。