Unsupervised Time Series Anomaly Detection

Unsupervised time series anomaly detection aims to identify unusual patterns in data without relying on pre-labeled examples, a crucial task across diverse fields. Current research emphasizes improving the robustness and interpretability of existing methods, particularly those based on autoencoders, transformers, and generative adversarial networks (GANs), while also addressing challenges like concept drift and the impact of noisy data. A significant focus is on developing more rigorous evaluation benchmarks and exploring techniques like dimensionality reduction and test-time adaptation to enhance performance and efficiency. These advancements are vital for reliable monitoring and predictive maintenance in various applications, from smart homes and industrial processes to healthcare and finance.

Papers

October 26, 2024

Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection
Ruyi Zhang, Hongzuo Xu, Songlei Jian, Yusong Tan, Haifang Zhou, Rulin Xu
Trading Devil Unsupervised Time Series Anomaly Detection Multiple Detector Anomaly Contamination

October 22, 2024

Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning
Kai Zhao, Zhihao Zhuang, Chenjuan Guo, Hao Miao, Yunyao Cheng, Bin Yang
Anomaly Data Unsupervised Time Series Anomaly Detection Unveiling Hidden Energy Anomaly False in Season Anomaly Suppression

October 21, 2024

MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast
Shiyan Hu, Kai Zhao, Xiangfei Qiu, Yang Shu, Jilin Hu, Bin Yang, Chenjuan Guo
Data Detection Multi Scale Joint Learning Unsupervised Time Series Anomaly Detection

June 16, 2024

Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask
Jingyu Xiao, Zhiyao Xu, Qingsong Zou, Qing Li, Dan Zhao, Dong Fang, Ruoyu Li, Wenxin Tang, Kang Li, Xudong Zuo, Penghui Hu, Yong Jiang, Zixuan Weng, Michael R. Lyv
Human SAFETY Based Anomaly Detection Behavior Model Smart Home Anomalous Behavior Unsupervised Time Series Anomaly Detection Mask Specific Loss

May 4, 2024

Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?
M. Saquib Sarfraz, Mei-Yen Chen, Lukas Layer, Kunyu Peng, Marios Koulakis
Product Specific Position Information Deep Anomaly Detection Unsupervised Time Series Anomaly Detection Novel Deep Learning Model

April 27, 2024

Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent Neighborhoods
Wenzhen Yue, Xianghua Ying, Ruohao Guo, DongDong Chen, Ji Shi, Bowei Xing, Yuqing Zhu, Taiyan Chen
Attention Mechanism Attention Matrix Time Series Anomaly Detection Anomaly Detection Benchmark Unsupervised Time Series Anomaly Detection Reconstruction Error Flat Lattice Transformer

April 3, 2024

End-To-End Self-tuning Self-supervised Time Series Anomaly Detection
Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefanía Serral Asensio, Jaemin Yoo, Leman Akoglu
Time Series Unsupervised Time Series Anomaly Detection Anomaly Type

March 7, 2024

Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series
Mahsun Altin, Altan Cakir
Anomaly Detection Dimensionality Reduction Multivariate Time Series External Influence Anomaly Detection Performance Unsupervised Time Series Anomaly Detection Transformer Based Anomaly Detection

February 5, 2024

Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
Zexin Wang, Changhua Pei, Minghua Ma, Xin Wang, Zhihan Li, Dan Pei, Saravan Rajmohan, Dongmei Zhang, Qingwei Lin, Haiming Zhang, Jianhui Li, Gaogang Xie
Anomaly Detection Variational Autoencoders Lung VAE Conditional Variational Autoencoder Anomaly Detection Model Frequency Perspective Unsupervised Time Series Anomaly Detection

December 19, 2023

When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection
Dongmin Kim, Sunghyun Park, Jaegul Choo
Full Model Test Time Adaptation Time Series Anomaly Detection Anomaly Detector Unsupervised Time Series Anomaly Detection

October 26, 2023

October 24, 2023

Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
Chih-Yu Lai, Fan-Keng Sun, Zhengqi Gao, Jeffrey H. Lang, Duane S. Boning
Anomaly Detection Exceptional Point Anomaly Score Unsupervised Time Series Anomaly Detection Incremental Reconstruction Rating Meaningfulness

October 9, 2023

LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Feiyi Chen, Zhen Qin, Yingying Zhang, Shuiguang Deng, Yi Xiao, Guansong Pang, Qingsong Wen
Constructive Approach Model Overfitting Light Work Time Series Anomaly Detection State of the Art Anomaly Model Retraining Unsupervised Time Series Anomaly Detection

March 22, 2023

TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks
Shyam Sundar Saravanan, Tie Luo, Mao Van Ngo
Anomaly Detection Unsupervised Anomaly Detection C Gan Temporal Pattern Unsupervised Time Series Anomaly Detection Input Time Series Cycle Consistent Generative Adversarial Network

December 6, 2022

Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods
Nesryne Mejri, Laura Lopez-Fuentes, Kankana Roy, Pavel Chernakov, Enjie Ghorbel, Djamila Aouada
General Analysis Time Series State of the Art Unsupervised Anomaly Detection Comprehensive Evaluation Time Series Anomaly Detection Unsupervised Time Series Anomaly Detection

December 5, 2022

Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection
Christopher P. Ley, Jorge F. Silva
Anomaly Detection Unsupervised Anomaly Detection Distortion Aware Lossy Compression Unsupervised Time Series Anomaly Detection Temporal Autoencoder

September 10, 2022

A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis
Yan Zhao, Liwei Deng, Xuanhao Chen, Chenjuan Guo, Bin Yang, Tung Kieu, Feiteng Huang, Torben Bach Pedersen, Kai Zheng, Christian S. Jensen
Anomaly Detection General Analysis Time Series Comparative Study Unsupervised Anomaly Detection Time Series Data Optical Experiment Fraud Detection Unsupervised Time Series Anomaly Detection

July 25, 2022

Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao, Yongjun Wang, Guansong Pang
Anomaly Detection Unsupervised Time Series Anomaly Detection Calibrated Learning Normality Learning Calibration Network

April 7, 2022

Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version
Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen, Yan Zhao, Feiteng Huang, Kai Zheng
Robust Version Outlier Detection Version Identification Unsupervised Time Series Anomaly Detection

Unsupervised Time Series Anomaly Detection

Papers

Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection

Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning

MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast

Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask

Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?

Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent Neighborhoods

End-To-End Self-tuning Self-supervised Time Series Anomaly Detection

Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series

Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective

When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection

Making the End-User a Priority in Benchmarking: OrionBench for Unsupervised Time Series Anomaly Detection

MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction

LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection

TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks

Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods

Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection

A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis

Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version