Scalable and user friendly neural :brain: forecasting algorithms.

Time series forecasting with machine learning models

Implement Reservoir Computing models for time series classification, clustering, forecasting, and much more!

An extension of LightGBM to probabilistic modelling

An open source library for Fuzzy Time Series in Python

[NeurIPS 2023] A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

Valid and adaptive prediction intervals for probabilistic time series forecasting.

Bayesian time series prediction

APDTFlow is a modern and extensible forecasting framework for time series data that leverages advanced techniques including neural ordinary differential equations (Neural ODEs), transformer-based components, and probabilistic modeling. Its modular design allows researchers and practitioners to experiment with multiple forecasting models and easily

A package for online distributional learning.

Code for paper "Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach" https://arxiv.org/abs/2206.02433

[NeurIPS 2025] Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting

Code for "Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts", IJCAI 2022

Predicting the probability of nocturnal hypoglycemic events with probabilistic forecasting.

AI models competing in prediction markets. Reality as the ultimate benchmark. Seven frontier LLMs forecast real-world events through Polymarket. No memorization possible - only genuine forecasting ability.

Probabilistic load forecasting with Reservoir Computing

Scalable Models of Probabilistic Forecasting with Fuzzy Time Series, PhD Thesis

T-DPnet-Transformer-based-deep-Probabilistic-network-for-load-forecasting

Code for Diffusion-based Decoupled Deterministic and Uncertain Framework for Probabilistic Multivariate Time Series Forecasting

Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting

Kernel quantile regression

This repository contains code and data for probabilistic forecasting of electricity loads.

Codes in this repository generate probabilistic forecasts of international migration flows between the 200 most populous countries.

Probabilistic Wind Power Forecasting with R

Cross-temporal probabilistic forecast reconciliation repository

Predicting Demand in Primary Health Care Centers in Lebanon: Insight from Syrian Refugees Crisis

A novel deep transfer learning framework for forecasting NavIC satellite ephemeris and clock errors. PRESTO overcomes extreme data scarcity using a hybrid architecture (GNNs + Semiparametric Decomposition + Autoformers) and synthetic data augmentation to generate normally distributed prediction residuals.

Qauntile autoregressive neural network for time series anamoly detection.

culearn: Cumulant Learning in Python

DeepAR implementation for seasonal influenza cases in German districts