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.

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

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

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

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

Unified Training of Universal Time Series Forecasting Transformers

proof of concept for a transformer-based time series prediction model

About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_

Official code, datasets and checkpoints for "Timer: Generative Pre-trained Transformers Are Large Time Series Models" (ICML 2024) and subsequent works

Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.

Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

Multivariate Time Series Transformer, public version

The best repository showing why transformers might not be the answer for time series forecasting and showcasing the best SOTA non transformer models.

Official implementation of our ICLR 2023 paper "Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting"

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (NeurIPS 2019)

Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415

Official implementation for "TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables" (NeurIPS 2024)

No description available

Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)

Time-Series-Optimized Transformer for Observability

This is an official implementation of "EnergyPatchTST: Multi-scale Time Series Transformers with Uncertainty Estimation for Energy Forecasting".

My implementation of the transformer architecture from the Attention is All you need paper applied to time series.

Repository of Transformer based PyTorch Time Series Models

PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network

Convolutional Transformer for time series

PyTorch implementation of Transformer model used in "Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case"

PyTorch implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

transformer/self-attention for Multidimensional time series forecasting 使用transformer架构实现多维时间预测