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

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

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

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

GNN for spatiotemporal Forecasting using Extreme Value Theory

Find The Tail - Matlab

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

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

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

Automatic EVT based thresholding for extreme value analysis using peaks over thresholds (POT)

An empirical analysis of financial time series, exploring heavy tails, volatility clustering, and risk measure estimation for the S&P 500 and Apple Inc.

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