VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
Fuente:
arXiv
Saved in:
| Main Authors: | Cencillo, Alberto D., Concepción, Leonardo, Triguero, Isaac, Luengo, Julián |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
by: Cencillo, Alberto D., et al.
Published: (2026)
by: Cencillo, Alberto D., et al.
Published: (2026)
Learning Unified Representations of Normalcy for Time Series Anomaly Detection
by: Sarker, Prithul, et al.
Published: (2026)
by: Sarker, Prithul, et al.
Published: (2026)
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
by: Park, Jinju, et al.
Published: (2026)
by: Park, Jinju, et al.
Published: (2026)
Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data
by: Kelleher, John D., et al.
Published: (2025)
by: Kelleher, John D., et al.
Published: (2025)
Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detection
by: Han, Chan Sik, et al.
Published: (2025)
by: Han, Chan Sik, et al.
Published: (2025)
An Improved Time Series Anomaly Detection by Applying Structural Similarity
by: Wang, Tiejun, et al.
Published: (2025)
by: Wang, Tiejun, et al.
Published: (2025)
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection
by: Zhang, Wenxin, et al.
Published: (2025)
by: Zhang, Wenxin, et al.
Published: (2025)
Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
by: Cho, Dongchan, et al.
Published: (2025)
by: Cho, Dongchan, et al.
Published: (2025)
Deep Learning for Time Series Anomaly Detection: A Survey
by: Darban, Zahra Zamanzadeh, et al.
Published: (2022)
by: Darban, Zahra Zamanzadeh, et al.
Published: (2022)
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection
by: Chen, Katrina, et al.
Published: (2023)
by: Chen, Katrina, et al.
Published: (2023)
Causal Disentanglement Learning for Accurate Anomaly Detection in Multivariate Time Series
by: Kim, Wonah, et al.
Published: (2025)
by: Kim, Wonah, et al.
Published: (2025)
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
by: Li, Mengxuan, et al.
Published: (2024)
by: Li, Mengxuan, et al.
Published: (2024)
Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection
by: Kim, HyunGi, et al.
Published: (2025)
by: Kim, HyunGi, et al.
Published: (2025)
Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
by: Chen, Yutong, et al.
Published: (2024)
by: Chen, Yutong, et al.
Published: (2024)
DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detection
by: Golchin, Bahareh, et al.
Published: (2025)
by: Golchin, Bahareh, et al.
Published: (2025)
Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series
by: Yuan, Hanyang, et al.
Published: (2024)
by: Yuan, Hanyang, et al.
Published: (2024)
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
by: Darban, Zahra Zamanzadeh, et al.
Published: (2024)
by: Darban, Zahra Zamanzadeh, et al.
Published: (2024)
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
by: Koumar, Josef, et al.
Published: (2024)
by: Koumar, Josef, et al.
Published: (2024)
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
by: Chen, Feiyi, et al.
Published: (2023)
by: Chen, Feiyi, et al.
Published: (2023)
Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection
by: Tang, Qideng, et al.
Published: (2026)
by: Tang, Qideng, et al.
Published: (2026)
Time Series Foundational Models: Their Role in Anomaly Detection and Prediction
by: Shyalika, Chathurangi, et al.
Published: (2024)
by: Shyalika, Chathurangi, et al.
Published: (2024)
Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
by: González, Gastón García, et al.
Published: (2025)
by: González, Gastón García, et al.
Published: (2025)
CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection
by: Zhong, Zhijie, et al.
Published: (2025)
by: Zhong, Zhijie, et al.
Published: (2025)
ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN
by: Bashar, Md Abul, et al.
Published: (2023)
by: Bashar, Md Abul, et al.
Published: (2023)
Matrix Profile for Anomaly Detection on Multidimensional Time Series
by: Yeh, Chin-Chia Michael, et al.
Published: (2024)
by: Yeh, Chin-Chia Michael, et al.
Published: (2024)
Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection
by: Zhang, Ruyi, et al.
Published: (2024)
by: Zhang, Ruyi, et al.
Published: (2024)
LLM-Assisted Logic Rule Learning: Scaling Human Expertise for Time Series Anomaly Detection
by: Zhang, Haoting, et al.
Published: (2026)
by: Zhang, Haoting, et al.
Published: (2026)
Semantic-Inductive Attribute Selection for Zero-Shot Learning
by: Herrera-Aranda, Juan Jose, et al.
Published: (2025)
by: Herrera-Aranda, Juan Jose, et al.
Published: (2025)
Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection
by: Özer, Kadir-Kaan, et al.
Published: (2026)
by: Özer, Kadir-Kaan, et al.
Published: (2026)
Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection
by: Baumgartner, David, et al.
Published: (2026)
by: Baumgartner, David, et al.
Published: (2026)
IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection
by: Zhou, Xiaohui, et al.
Published: (2026)
by: Zhou, Xiaohui, et al.
Published: (2026)
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations
by: Hao, Xinli, et al.
Published: (2024)
by: Hao, Xinli, et al.
Published: (2024)
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
by: Xu, Hongzuo, et al.
Published: (2022)
by: Xu, Hongzuo, et al.
Published: (2022)
RATFM: Retrieval-augmented Time Series Foundation Model for Anomaly Detection
by: Maru, Chihiro, et al.
Published: (2025)
by: Maru, Chihiro, et al.
Published: (2025)
Weighted Contrastive Learning for Anomaly-Aware Time-Series Forecasting
by: Ekstrand, Joel, et al.
Published: (2025)
by: Ekstrand, Joel, et al.
Published: (2025)
Dive into Time-Series Anomaly Detection: A Decade Review
by: Boniol, Paul, et al.
Published: (2024)
by: Boniol, Paul, et al.
Published: (2024)
TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly Detection with Inexact Supervision
by: Liu, Chen, et al.
Published: (2024)
by: Liu, Chen, et al.
Published: (2024)
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System
by: Li, Pengyu, et al.
Published: (2024)
by: Li, Pengyu, et al.
Published: (2024)
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework
by: Ghanim, Jihan, et al.
Published: (2024)
by: Ghanim, Jihan, et al.
Published: (2024)
Contrastive Time Series Forecasting with Anomalies
by: Ekstrand, Joel, et al.
Published: (2025)
by: Ekstrand, Joel, et al.
Published: (2025)
Similar Items
-
Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
by: Cencillo, Alberto D., et al.
Published: (2026) -
Learning Unified Representations of Normalcy for Time Series Anomaly Detection
by: Sarker, Prithul, et al.
Published: (2026) -
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
by: Park, Jinju, et al.
Published: (2026) -
Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data
by: Kelleher, John D., et al.
Published: (2025) -
Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detection
by: Han, Chan Sik, et al.
Published: (2025)