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 curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources

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

[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.

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

Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT

A Vision Transformer Network for Image Anomaly Detection and Localization

A Pytorch implement of paper "Anomaly detection in dynamic graphs via transformer" (TADDY).

VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer (CVPR 2026)

This is unofficial implementation of Inpainting Transformer for Anomaly Detection(Intra)

Code for "Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection" published in NeurIPS 2023

This is an official implement for "HSTforU: Anomaly Detection in Aerial and Ground-based Videos with Hierarchical Spatio-Temporal Transformer for U-net"

Code for the paper "Anomaly-Based Intrusion Detection in IIoT Networks Using Transformer Models"

Gated Transformer for Hyperspectral Anomaly Detection

Official Implementation of Mamba Adaptive Anomaly Transformer (MAAT) - paper is now available in Engineering Applications of Artificial Intelligence Journal: https://doi.org/10.1016/j.engappai.2025.111685

The code of Self-Training Multi-Sequence Learning with Transformer for Weakly Supervised Video Anomaly Detection

A Transformer-based GAN for Anomaly Detection, International Conference on Artificial Neural Networks, (ICANN2022).

Sharkticon is an anomaly detection system, it analyzes your network using a Transformers model adapted to the anomaly detection.

A Unified Framework for Detecting Point and Collective Anomalies in Operating System Logs via Collaborative Transformers

Anomaly detection from OS logs using Transformers implemented with Pytorch.

S.-S. Young, Chia-Hsiang Lin, et al., “Unsupervised abundance matrix reconstruction transformer guided fractional attention mechanism for hyperspectral anomaly detection,” IEEE Transactions on Neural Networks and Learning Systems, 2024.

[AIR-DISCOVER Summer Research] Multi-class Anomaly Detection Transformer with Heterogenous Knowledge Distillation

Official code for "TAEF: Transformer-Based Autoencoder Framework for Nonlinear Hyperspectral Anomaly Detection Detection" [TGRS2024]

No description available

A Multi-scale Transformers with Contrastive Learning for UAV anomaly detection with flight data (MTCL-UAV)

No description available

TFD-Net: Transformer Deviation Network for Weakly Supervised Anomaly Detection

An end-to-end Transformer for geochemical anomaly detection

ADFormer: Generalizable Few-Shot Anomaly Detection with Dual CNN-Transformer Architecture

Official repository for the paper "RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection"