TODS: An Automated Time-series Outlier Detection System

A toolkit for time series machine learning and deep learning

Awesome Easy-to-Use Deep Time Series Modeling based on PaddlePaddle, including comprehensive functionality modules like TSDataset, Analysis, Transform, Models, AutoTS, and Ensemble, etc., supporting versatile tasks like time series forecasting, representation learning, and anomaly detection, etc., featured with quick tracking of SOTA deep models.

The official code 👩‍💻 for - TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

Time series anomaly detection algorithm implementations for TimeEval (Docker-based)

GutenTAG is an extensible tool to generate time series datasets with and without anomalies; integrated with TimeEval.

PyTorch implementation of "Drift doesn't Matter: Dynamic Decomposition with Dffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection" (NeurIPS 2023)

A simple-to-use Python tool for time series anomaly detection!

Domain Adaptation Contrastive learning model for Anomaly Detection in multivariate time series (DACAD), combining UDA with contrastive learning. DACAD utilizes an anomaly injection mechanism that enhances generalization across unseen anomalous classes, improving adaptability and robustness.

[Read-Only Mirror] Benchmarking Toolkit for Time Series Anomaly Detection Algorithms using TimeEval and GutenTAG

[ICDE'2024] Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection

Official implementation of ECML PKDD'24 paper 'Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection'.

Time series data contribution via influence functions

The official PyTorch implementation of our NeurIPS'25 paper: Synthetic Series-Symbol Data Generation for Time Series Foundation Models.

Precursor-of-Anomaly Detection

KDSelector proposes a novel knowledge-enhanced and data-efficient framework for learning a neural network-based model selector in the context of time series anomaly detection.

Time Series Forecasting using RNN, Anomaly Detection using LSTM Auto-Encoder and Compression using Convolutional Auto-Encoder

Time series anomaly detection, time series classification & dynamic time warping, performed on a dataset of Canadian weather measurements.

LSTM-based Auto-Encoder for Anomaly Detection of Streaming Time Series