Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series 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.

Statsmodels: statistical modeling and econometrics in Python

Fast and Accurate ML in 3 Lines of Code

A unified framework for machine learning with time series

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

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

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

Postgres with GPUs for ML/AI apps.

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

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

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

Free Weather Forecast API for non-commercial use

Chronos: Pretrained Models for Time Series Forecasting

Time series forecasting with PyTorch

Lightning ⚡️ fast forecasting with statistical and econometric models.

Merlion: A Machine Learning Framework for Time Series Intelligence

NeuralProphet: A simple forecasting package

Scalable and user friendly neural :brain: forecasting algorithms.

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 🚀.

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.

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

List of papers, code and experiments using deep learning for time series forecasting

[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008