TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

Provide an input CSV and a target field to predict, generate a model + code to run it.

Model Predictive Contouring Controller (MPCC) for Autonomous Racing

The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

This repository features an Energy Optimization System (EOS) that optimizes energy distribution, usage for batteries, heat pumps& household devices. It includes predictive models for electricity prices (planned), load forecasting& dynamic optimization to maximize energy efficiency & minimize costs. Founder Dr. Andreas Schmitz (YouTube @akkudoktor)

Model predictive control python toolbox

Pytorch implementation of Transfusion, "Predict the Next Token and Diffuse Images with One Multi-Modal Model", from MetaAI

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

Fast and embedded solvers for nonlinear optimal control and nonlinear model predictive control

Code for a dynamic multilevel Bayesian model to predict US presidential elections. Written in R and Stan.

Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at contact@unitary.ai.

Cosmos-Predict2.5, the latest version of the Cosmos World Foundation Models (WFMs) family, specialized for simulating and predicting the future state of the world in the form of video.

A fast and differentiable model predictive control (MPC) solver for PyTorch.

Model-predictive control for microcontrollers

Differential Wheeled Mobile Robot - Nonlinear Model Predictive Control based on ROS

Code and models accompanying "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning"

This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.

KAPAO is an efficient single-stage human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.

Code and Resources for "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Kuhn and Johnson

HungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!

Python implementation of MPPI (Model Predictive Path-Integral) controller to understand the basic idea. Mandatory dependencies are numpy and matplotlib only.

DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

Model Predictive Path Integral (MPPI) with approximate dynamics implemented in pytorch

Reinforcement Learning with Model Predictive Control

The mpc_local_planner package implements a plugin to the base_local_planner of the 2D navigation stack. It provides a generic and versatile model predictive control implementation with minimum-time and quadratic-form receding-horizon configurations.

Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.

Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.