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Copula in temporal data mining: The joint return period of extreme temperature in Beijing

机译：在时间数据挖掘中的copula：北京极端温度的联合返回期

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Copula has become a popular tool in multivariate modeling widely applied in lots of fields, but less used in temporal data. The analysis of the extreme temperature is an important part of the study in climate change, and the data of extreme temperature is one of the temporal data. So in this study, copula is used to calculate the joint return period of extreme temperature (from station in Beijing) with the indices Frost Days (FD and Summer Days (SU35). We used Anderson-Darling goodness-of-fit test (A-D test) to find the most fitted probability distribution and evaluate the 10-year return period, 50-year return period and 100-year return period based on the marginal distribution of the two univariate. After calculating the joint return period, we compared the results of univariate return period and joint return period with the reality. The results show that, the joint return period is more accurate than the univariate period, and by improving both the choice of indices and the copula method, the results should closer to the reality. This study is of significance to get a better understanding in temporal data mining by using copula method.
机译：Copula已成为多变量建模的流行工具，广泛应用于许多字段，但较少在时间数据中使用。对极端温度的分析是气候变化研究的重要组成部分，极端温度的数据是时间数据之一。所以在这项研究中，Copula用于计算极端温度（北京站）的联合返回期与索尔霜天（FD和夏季）（SU 35 ）。我们使用的是Anderson-Darling健康测试（AD测试）以找到最拟合的概率分布，并根据两年单变量的边际分布评估10年回报期，50年回报期和100年回报期。计算后联合返回期，与现实相比，将单变量回报期和联合返回期的结果进行比较。结果表明，联合返回期比单变量更准确，并通过改善指数和拷贝方法的选择，结果应更接近现实。本研究具有重要意义，以通过使用Copula方法在时间数据挖掘中获得更好的理解。

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