This paper describes the estimation of the extreme spatio-temporal sea surface temperature data based on the quantile factor model implemented by the SNU multiscale team. The proposed method was developed for the EVA2019 Data Challenge. Various attempts have been conducted to use factor models in spatio-temporal data analysis to find hidden factors in high-dimensional data. Factor models represent high-dimensional data as a linear combination of several factors, and hence, can describe spatially and temporally correlated data in a simple form. Meanwhile, unlike ordinary factor models, there are asymmetric norm-based factor models, such as quantile factor models or expectile dynamic semiparametric factor models, that can help understand the quantitative behavior of data beyond their mean structure. For this purpose, we apply a quantile factor model to the data to obtain significant factors explaining the quantile response of the temperatures and find quantile estimates. We develop a new method for inference of quantiles of extremal levels by extrapolating quantile estimates from the factor model with extreme value theory. The proposed method provides better performance than the benchmark, gives some interpretable insights, and shows the potential to expand the factor model with various data.

本文介绍了基于SNU多尺度团队实施的分位数因子模型对极端时空海面温度数据的估计。提出的方法是为EVA2019数据挑战开发的。已经进行了各种尝试，以在时空数据分析中使用因子模型来查找高维数据中的隐藏因子。因子模型将高维数据表示为几个因子的线性组合，因此可以以简单的形式描述时空相关的数据。同时，与普通因子模型不同，存在基于非对称范数的因子模型，例如分位数因子模型或预期动态半参数因子模型，这些模型可以帮助理解超出其均值结构的数据的定量行为。以此目的，我们将分位数因子模型应用于数据，以获得解释温度分位数响应的重要因子并找到分位数估计。通过用极值理论从因子模型中推断分位数估计，我们开发了一种推断极值水平分位数的新方法。所提出的方法提供了比基准更好的性能，提供了一些可解释的见解，并显示了使用各种数据扩展因子模型的潜力。