Cover Image

Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Hongzuo, Wang, Yijie, Jian, Songlei, Liao, Qing, Wang, Yongjun, Pang, Guansong
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection
by: Zhang, Ruyi, et al.
Published: (2024)

IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection
by: Zhou, Xiaohui, et al.
Published: (2026)

Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
by: Chen, Yutong, et al.
Published: (2024)

Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling
by: Zhao, Sinong, et al.
Published: (2024)

Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection
by: Qiao, Hezhe, et al.
Published: (2023)

Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection
by: Chen, Feiyi, et al.
Published: (2025)

LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection
by: Wang, Dezheng, et al.
Published: (2026)

AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection
by: Huang, Tiange, et al.
Published: (2025)

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
by: Qiao, Hezhe, et al.
Published: (2025)

RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
by: Mou, Xudong, et al.
Published: (2025)

Deep Graph Anomaly Detection: A Survey and New Perspectives
by: Qiao, Hezhe, et al.
Published: (2024)

Open-Set Graph Anomaly Detection via Normal Structure Regularisation
by: Wang, Qizhou, et al.
Published: (2023)

Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series
by: Yuan, Hanyang, et al.
Published: (2024)

Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection
by: Tang, Qideng, et al.
Published: (2026)

Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection
by: Zhong, Zhijie, et al.
Published: (2025)

An Improved Time Series Anomaly Detection by Applying Structural Similarity
by: Wang, Tiejun, et al.
Published: (2025)

ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection
by: Hermary, Romain, et al.
Published: (2026)

An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework
by: Ghanim, Jihan, et al.
Published: (2024)

Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?
by: Sarfraz, M. Saquib, et al.
Published: (2024)

Disentangling Tabular Data Towards Better One-Class Anomaly Detection
by: Ye, Jianan, et al.
Published: (2024)

Unsupervised Surrogate Anomaly Detection
by: Klüttermann, Simon, et al.
Published: (2025)

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
by: Jin, Ming, et al.
Published: (2023)

Matrix Profile for Anomaly Detection on Multidimensional Time Series
by: Yeh, Chin-Chia Michael, et al.
Published: (2024)

AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds
by: Wang, Qizhou, et al.
Published: (2025)

CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection
by: Wang, Xiaolei, et al.
Published: (2024)

Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark
by: Fawaz, Hassan Ismail, et al.
Published: (2023)

One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
by: Gao, Mengzhou, et al.
Published: (2026)

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
by: Zhang, Junru, et al.
Published: (2026)

TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
by: Li, Mengxuan, et al.
Published: (2024)

Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
by: Bei, Yuanchen, et al.
Published: (2024)

CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
by: Koumar, Josef, et al.
Published: (2024)

Global Feature Enhancing and Fusion Framework for Strain Gauge Time Series Classification
by: Zhang, Xu, et al.
Published: (2025)

CAPMix: Robust Time Series Anomaly Detection Based on Abnormal Assumptions with Dual-Space Mixup
by: Mou, Xudong, et al.
Published: (2025)

Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values
by: Zheng, Yu, et al.
Published: (2024)

Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification
by: Feofanov, Vasilii, et al.
Published: (2025)

LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
by: Chen, Feiyi, et al.
Published: (2023)

Deep Learning for Time Series Anomaly Detection: A Survey
by: Darban, Zahra Zamanzadeh, et al.
Published: (2022)

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction
by: Shyalika, Chathurangi, et al.
Published: (2024)

Learning Unified Representations of Normalcy for Time Series Anomaly Detection
by: Sarker, Prithul, et al.
Published: (2026)

Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
by: González, Gastón García, et al.
Published: (2025)