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In many applications of time series models, such as climate analysis andsocial media analysis, we are often interested in extreme events, such asheatwave, wind gust, and burst of topics. These time series data usuallyexhibit a heavy-tailed distribution rather than a Gaussian distribution. Thisposes great challenges to existing approaches due to the significantlydifferent assumptions on the data distributions and the lack of sufficient pastdata on extreme events. In this paper, we propose the Sparse-GEV model, alatent state model based on the theory of extreme value modeling toautomatically learn sparse temporal dependence and make predictions. Our modelis theoretically significant because it is among the first models to learnsparse temporal dependencies among multivariate extreme value time series. Wedemonstrate the superior performance of our algorithm to the state-of-artmethods, including Granger causality, copula approach, and transfer entropy, onone synthetic dataset, one climate dataset and two Twitter datasets.

Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling