This repository implements the Anomaly Transformer, a deep learning approach for detecting anomalies in time series data. It introduces an Association Discrepancy criterion derived from a novel Anomaly-Attention mechanism to distinguish normal from abnormal points. The method uses a minimax strategy to amplify the distinguishability of the association discrepancy during training. Released with PyTorch, it provides training scripts and pre-processed benchmark datasets for reproducibility.