Web: http://arxiv.org/abs/2205.02441

May 6, 2022, 1:11 a.m. | Asadullah Hill Galib, Andrew McDonald, Tyler Wilson, Lifeng Luo, Pang-Ning Tan

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv data deep deep learning forecasting learning time time series

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