The extraction of individual events from continuous time series is a common challenge in many extreme value studies. In the field of environmental science, various methods and algorithms for event identification (de-clustering) have been applied in the past. The distinctive features of extreme events, such as their temporal evolutions, durations, and inter-arrival times, vary significantly from one location to another making it difficult to identify independent events in the series. In this study, we propose a new automated approach to detect independent events from time series, by identifying the standard event duration across locations using event correlations. To account for the inherent variability at a given site, we incorporate the standard deviation of the event duration through a soft-margin approach. We apply the method to 1 485 tide gauge records from across the global coast to gain new insights into the typical durations of independent storm surges along different coastline stretches. The results highlight the effects of both local characteristics at a given tide gauge and seasonality on the derived storm durations. Additionally, we compare the results obtained with other commonly used de-clustering techniques showing that these methods are more sensitive to the chosen threshold.

中文翻译：

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使用相关分析对风暴潮时间序列进行去聚类


从连续时间序列中提取个体事件是许多极值研究中的常见挑战。在环境科学领域，过去已经应用了各种事件识别（去聚类）的方法和算法。极端事件的显着特征，例如它们的时间演变、持续时间和到达间隔时间，在不同地点之间存在显着差异，因此很难识别该系列中的独立事件。在本研究中，我们提出了一种新的自动化方法，通过使用事件相关性识别跨位置的标准事件持续时间来检测时间序列中的独立事件。为了解释给定站点的固有变异性，我们通过软裕度方法合并了事件持续时间的标准偏差。我们将该方法应用于全球海岸的 1 485 条验潮仪记录，以获得对沿不同海岸线延伸的独立风暴潮的典型持续时间的新见解。结果强调了给定潮位计的当地特征和季节性对导出的风暴持续时间的影响。此外，我们将获得的结果与其他常用的去聚类技术进行比较，表明这些方法对所选阈值更敏感。