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

arXiv: https://arxiv.org/abs/2205.02441 · HTML: https://mlsanity.com/paper/2205.02441/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 2 of 2)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Deep Learning for Energy Markets](https://mlsanity.com/paper/1808.05527/) (2018)

## Built on by (most cited first, 2 of 2)

- [Neural Bayes estimators for censored inference with peaks-over-threshold models](https://mlsanity.com/paper/2306.15642/) (2023)
- [FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models](https://mlsanity.com/paper/2505.12167/) (2025)

## Abstract (arXiv)

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.