Popular GitHub Repositories for Load Forecasting

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

Fast and Accurate ML in 3 Lines of Code

A unified framework for machine learning with time series

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

A python library for user-friendly forecasting and anomaly detection on time series.

Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

Postgres with GPUs for ML/AI apps.

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

Probabilistic time series modeling in Python

Time series forecasting with PyTorch

Merlion: A Machine Learning Framework for Time Series Intelligence

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

NeuralProphet: A simple forecasting package

Scalable and user friendly neural :brain: forecasting algorithms.

Chronos: Pretrained Models for Probabilistic Time Series Forecasting

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 ?.

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.