  Accurate forecasting of extreme values in time series is critical due to the
significant impact of extreme events on human and natural systems. This paper
presents DeepExtrema, a novel framework that combines a deep neural network
(DNN) with generalized extreme value (GEV) distribution to forecast the block
maximum value of a time series. Implementing such a network is a challenge as
the framework must preserve the inter-dependent constraints among the GEV model
parameters even when the DNN is initialized. We describe our approach to
address this challenge and present an architecture that enables both
conditional mean and quantile prediction of the block maxima. The extensive
experiments performed on both real-world and synthetic data demonstrated the
superiority of DeepExtrema compared to other baseline methods.
