On Choice of Hyper-parameter in Extreme Value Theory based on Machine Learning Techniques

TL;DR

This paper introduces a novel method for selecting hyper-parameters in extreme value theory by leveraging machine learning techniques, demonstrating its effectiveness on real-world data.

Contribution

It proposes a new hyper-parameter selection method for EVT that integrates machine learning, filling a gap in practical application of EVT.

Findings

The method shows improved hyper-parameter tuning in EVT.

Experimental results on real data validate the approach.

The approach enhances EVT's usability in real-world scenarios.

Abstract

Extreme value theory (EVT) is a statistical tool for analysis of extreme events. It has a strong theoretical background, however, we need to choose hyper-parameters to apply EVT. In recent studies of machine learning, techniques of choosing hyper-parameters have been well-studied. In this paper, we propose a new method of choosing hyper-parameters in EVT based on machine learning techniques. We also experiment our method to real-world data and show good usability of our method.