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arar

ararforecast.jl

Forecasting using Arar algorithm

autoquant

R package for automation of machine learning, forecasting, feature engineering, model evaluation, and model interpretation

biogpt

caretforecast

Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms

causalimpact

An R package for causal inference in time series

conformal-predictions-from-scratch

Various Conformal Prediction methods implemented from scratch in pure NumPy for an educational purpose.

conformalprediction.jl

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

conformalregressor.jl

copulacpts

Code for Copula conformal prediction paper (ICLR 2024)

deeptime

PyTorch code for DeepTIMe: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting

dejavu.jl

Julia implementation of Déjà vu: A data-centric forecasting approach through time series cross-similarity

easyforecast

Forecasting methods and algorithms from Introduction to Time Series and Forecasting by Peter J. BrockwellRichard A. Davis.

esrnn_torch

example-time-series-datasets

Example time series

fforma

R package for Feature-based Forecast Model Averaging

iif_workshop_open_source_for_forecasting

intermittentdemand.jl

IntermittentDemand.jl: Intermittent demand forecasting in Julia Language, |forecasting|Julia|

lcp

llmtime

mapie

A scikit-learn-compatible module for estimating prediction intervals.

mefm-package

R package to implement the MEFM

metrics.jl

Evaluation Metrics for Machine Learning in Julia

micecon

Microeconometric analysis of social housing in Austria.

ngboost

An R interface to the NGBoost.

ngboostforecast

Probabilistic Time Series Forecasting

ott

Optimal Transport tools implemented with the JAX framework, to get auto-diff, parallel and jit-able computations.

planr

probcast

ProbCast: an R Package for Flexible Probabilistic Forecasting

scoregradpred

Code for *ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models*