Scalable and user friendly neural 🧠 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

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

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

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

Predicting the probability of nocturnal hypoglycemic events with probabilistic forecasting.

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

Probabilistic load forecasting with Reservoir Computing

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

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

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