Automatic optimal sequential investment decisions. Forecasts made using advanced stochastic processes with Monte Carlo simulation. Dependency is handled with vine copulas.

Code for the WSDM 2021 paper "FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection".

This project studies the effects of the shape parameter estimator uncertainty at different threshold levels on the value-at-risk confidence interval for quantitative risk management (QRM) using the Generalized Pareto Distribution (GPD) from the Extreme Value Theory (EVT) approach.

DPhil project: Extreme value theory and GANs to generate compound coastal hazards (wind speed + sea level pressure) from ERA5 reanalysis data over the Bay of Bengal. In development...

Pure-Python library of heavy-tailed probability distributions (Pareto, Burr, LogNormal, etc.) built from first principles.

Python package for fitting statistical models using calibrating priors.

EVT-based noise injection toolkit for evaluating time series forecasting robustness

Potential Height Python packages: runs the experiments for "Finding the potential height of tropical cyclone storm surges in a changing climate using Bayesian optimization"

Estimate tail parameters of heavy-tailed distributions (including power law exponent gamma) in Python

Dependence-aware block-maxima inference for severity, persistence, and design-life levels.

GNN for spatiotemporal Forecasting using Extreme Value Theory

Find The Tail - Matlab

A specialized Python library for sparse multivariate extreme value analysis, structure learning, and robust spectral measure estimation using extremal graphical models.

A Rust library and command-line tool for analyzing Power-Law distributions in empirical data.

A deep study of human longevity using demographic data (HLD, IDL) and Extreme Value Theory to assess the potential existence of a theoretical limit to human lifespan

Two-stage frequentist framework for fusing sparse observations with dense simulations in spatial extreme value analysis

R package for estimation of elliptical extreme quantile regions

An End-to-End Python implementation of Köhler et al.'s (2026) orthogonalized tail-risk framework. Combines PCA-whitening spectral decomposition with Peaks-Over-Threshold EVT to quantify extreme risks in 479-dimensional financial networks. Implements Ferro-Segers clustering, dynamic residualization, and out-of-core processing for 2.6B+ data points.

This repo serves as a guide for understanding what extreme value theory is, using the genextreme package.