A library to model multivariate data using copulas.

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series, from ServiceNow Research

Python package for canonical vine copula trees with mixed continuous and discrete marginals

Code for paper "Copula-based conformal prediction for Multi-Target Regression"

Copula fitting in Python.

This repository contains the code of our published work in IEEE JBHI. Our main objective was to demonstrate the feasibility of the use of synthetic data to effectively train Machine Learning algorithms, prooving that it benefits classification performance most of the times.

Examples of scheduled jobs estimating copulas at www.microprediction.org

This repository contains the code for "Diffusion and Flow-based Copulas: Forgetting and Remembering Dependencies" accepted at ICLR 26.

Python library for modelling complex multivariate dependencies using stochastic copulas

Testing Pydantic, FastAPI, polyfactory, pandera and GraphQL with SQLModel and pydantic-mongo

From A to Z

Flow-based PC algorithm for causal discovery using Normalizing Flows

An institutional-grade statistical arbitrage engine leveraging Copula functions to model tail dependence and execute pairs trading strategies during market dislocations.

Multivariate time series generator based on the Phase Annealing algorithm. Various objective functions that focus on multivariate copula properties while annealing. Various plotting routines to visualize results. Take a look at the scripts in the "test" directory for how to use.

Portfolio construction and risk library, spanning hierarchical risk parity and nested clustering over denoised covariance, convex CVaR, EVaR and CDaR programs, Student-t copula tails, and extreme-value backtesting.