In many applications of time series models, such as climate analysis and
social media analysis, we are often interested in extreme events, such as
heatwave, wind gust, and burst of topics. These time series data usually
exhibit a heavy-tailed distribution rather than a Gaussian distribution. This
poses great challenges to existing approaches due to the significantly
different assumptions on the data distributions and the lack of sufficient past
data on extreme events. In this paper, we propose the Sparse-GEV model, a
latent state model based on the theory of extreme value modeling to
automatically learn sparse temporal dependence and make predictions. Our model
is theoretically significant because it is among the first models to learn
sparse temporal dependencies among multivariate extreme value time series. We
demonstrate the superior performance of our algorithm to the state-of-art
methods, including Granger causality, copula approach, and transfer entropy, on
one synthetic dataset, one climate dataset and two Twitter datasets.

本研究提出了 Sparse-GEV 模型，即基于极端值建模的潜在状态模型，以自动学习稀疏的时间依赖性并进行预测。该模型是学习多元极值时间序列稀疏时间依赖性的第一批模型之一。实验结果表明，我们的算法在一个模拟数据集，一个气候数据集和两个 Twitter 数据集上比最先进的方法，包括 Granger 因果性，copula 方法和转移熵，表现更优越。