time-series-forecasting

Chronos: Pretrained Models for Time Series Forecasting

Probabilistic time series modeling in Python

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"

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

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

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance, with 460+ scenarios, 5 specialist agents (IoT, FMSR, TSFM, Work Order,...), and multi-agent orchestration blueprints (MetaAgent, AgentHive) over MCP.

Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight)

[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"

[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Unified Training of Universal Time Series Forecasting Transformers

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

An Awesome List of the latest time series papers and code from top AI venues.

list of papers, code, and other resources

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)

[ICLR 2025 Spotlight] Official implementation of "Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts"

tfts: Time Series Deep Learning Models in TensorFlow

[TPAMI 2024] Awesome Resources of GNNs for Time Series Analysis (GNN4TS)

MOMENT: A Family of Open Time-series Foundation Models, ICML'24

Resources about time series forecasting and deep learning.

Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.

Resources for working with time series and sequence data

Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch

Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models

TimeCopilot: the GenAI Forecasting Agent. Built on LLMs and Time Series Foundation Models, it lets you forecast, cross-validate, and detect anomalies using multiple foundation models through a single API. From finance and energy to web analytics, TimeCopilot turns natural-language queries into production-ready forecasts.