DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data

TL;DR

DeepExtrema introduces a deep learning framework that effectively forecasts block maxima in time series by integrating neural networks with GEV distribution, outperforming existing methods in accuracy.

Contribution

The paper proposes a novel deep neural network architecture that preserves GEV parameter constraints for accurate extreme value forecasting.

Findings

DeepExtrema outperforms baseline methods on real-world data.

The framework accurately predicts both mean and quantiles of block maxima.

Experimental results validate the effectiveness of the proposed approach.

Abstract

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.