Alternatives and similar repositories for gpforecasting

Users that are interested in gpforecasting are comparing it to the libraries listed below

• datasig-ac-uk / esig
  esig python package
  ☆48Updated 5 months ago
• StatMixedML / DGBM
  Distributional Gradient Boosting Machines
  ☆27Updated 2 years ago
• simonmoesorensen / neural-ode-project
  Deeplearning project at The Technological University of Denmark (DTU) about Neural ODEs for finding dynamics in ordinary differential equ…
  ☆16Updated 3 years ago
• markvdw / RobustGP
  Robust initialisation of inducing points in sparse variational GP regression models.
  ☆33Updated 2 years ago
• PrincetonLIPS / mf-bocd
  Active multi-fidelity Bayesian online changepoint detection.
  ☆17Updated 4 years ago
• g-benton / Volt
  Public Implementation of Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
  ☆49Updated 2 years ago
• wesselb / gpar
  Implementation of the Gaussian Process Autoregressive Regression Model
  ☆66Updated 4 months ago
• gerdm / bayes
  Neat Bayesian machine learning examples
  ☆58Updated 4 months ago
• HerreraKrachTeichmann / NJODE
  code for "Neural Jump Ordinary Differential Equations"
  ☆30Updated 2 years ago
• secondmind-labs / markovflow
  A Tensorflow based library for Time Series Modelling with Gaussian Processes
  ☆32Updated 11 months ago
• jdb78 / lipo
  Global, derivative-free optimization for hyperparameter tuning
  ☆42Updated 2 years ago
• kamilest / conformal-rnn
  Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.
  ☆74Updated 6 months ago
• pierresegonne / VINF
  Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen
  ☆25Updated 4 years ago
• tgcsaba / GPSig
  Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
  ☆47Updated 2 years ago
• SigCGANs / Sig-Wasserstein-GANs
  ☆26Updated last year
• vr308 / Generalised-Gaussian-Processes
  Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods
  ☆22Updated last year
• pmorenoz / RecyclableGP
  Recyclable Gaussian Processes
  ☆11Updated 2 years ago
• AaltoML / scalable-inference-in-sdes
  Methods and experiments for assumed density SDE approximations
  ☆12Updated 3 years ago
• datasig-ac-uk / signature_applications
  Example applications of path signatures
  ☆39Updated 2 months ago
• Tom-Ryder / VIforSDEs
  A variational method for fast, approximate inference for stochastic differential equations.
  ☆44Updated 7 years ago
• deepcharles / tutorials-mva-2022
  Tutorials for the Machine Learning for Time Series class - Master MVA (2021/2022)
  ☆11Updated 3 years ago
• jessegrabowski / statespace-presentation
  Presentation materials for PyMC Statespace
  ☆22Updated 9 months ago
• vincentblot28 / conformalized_gp
  ☆48Updated 4 months ago
• elephaint / pedpf
  Parameter Efficient Deep Probabilistic Forecasting
  ☆14Updated 3 years ago
• TyXe-BDL / TyXe
  ☆153Updated 2 years ago
• jambo6 / neuralRDEs
  Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)
  ☆118Updated 4 years ago
• ml-jku / HopCPT
  Conformal Prediction for Time Series with Modern Hopfield Networks
  ☆79Updated last week
• crispitagorico / sigkernel
  Differentiable computations for the signature-PDE-kernel on CPU and GPU.
  ☆55Updated last year
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆42Updated 10 months ago
• karalets / TyXe
  Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)
  ☆42Updated 3 years ago