Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained Intra- and Inter-Variate Dependencies
Fuente:
arXiv
Saved in:
| Main Authors: | Xie, Yongzheng, Zhang, Hongyu, Babar, Muhammad Ali |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
by: Liu, Juncheng, et al.
Published: (2024)
by: Liu, Juncheng, et al.
Published: (2024)
Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift
by: He, Hui, et al.
Published: (2024)
by: He, Hui, et al.
Published: (2024)
Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
by: Cho, Dongchan, et al.
Published: (2025)
by: Cho, Dongchan, et al.
Published: (2025)
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)
Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection
by: Baumgartner, David, et al.
Published: (2026)
by: Baumgartner, David, et al.
Published: (2026)
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)
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)
Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values
by: Zheng, Yu, et al.
Published: (2024)
by: Zheng, Yu, et al.
Published: (2024)
MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection
by: Liu, Jinyang, et al.
Published: (2024)
by: Liu, Jinyang, et al.
Published: (2024)
Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis
by: Liu, Zhe, et al.
Published: (2024)
by: Liu, Zhe, et al.
Published: (2024)
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
by: Zhou, Xiaona, et al.
Published: (2025)
by: Zhou, Xiaona, et al.
Published: (2025)
Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection
by: Huang, Xiaoyu, et al.
Published: (2024)
by: Huang, Xiaoyu, et al.
Published: (2024)
CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift
by: Kim, HyunGi, et al.
Published: (2026)
by: Kim, HyunGi, et al.
Published: (2026)
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)
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
by: Yu, Guoqi, et al.
Published: (2024)
by: Yu, Guoqi, et al.
Published: (2024)
Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series
by: Chen, Kuan-Cheng, et al.
Published: (2025)
by: Chen, Kuan-Cheng, et al.
Published: (2025)
SEED: Spectral Entropy-Guided Evaluation of SpatialTemporal Dependencies for Multivariate Time Series Forecasting
by: Xiong, Feng, et al.
Published: (2025)
by: Xiong, Feng, et al.
Published: (2025)
DARTs: A Dual-Path Robust Framework for Anomaly Detection in High-Dimensional Multivariate Time Series
by: Liu, Xuechun, et al.
Published: (2025)
by: Liu, Xuechun, et al.
Published: (2025)
Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approach
by: Golchin, Bahareh, et al.
Published: (2025)
by: Golchin, Bahareh, et al.
Published: (2025)
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection
by: Huang, Tiange, et al.
Published: (2025)
by: Huang, Tiange, et al.
Published: (2025)
VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting
by: Zhang, Xudong, et al.
Published: (2026)
by: Zhang, Xudong, et al.
Published: (2026)
Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction
by: Hassan, Atif, et al.
Published: (2025)
by: Hassan, Atif, et al.
Published: (2025)
Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting
by: Wang, Muyao, et al.
Published: (2024)
by: Wang, Muyao, et al.
Published: (2024)
Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors
by: Gallon, R., et al.
Published: (2024)
by: Gallon, R., et al.
Published: (2024)
Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
by: Febrinanto, Falih Gozi, et al.
Published: (2023)
by: Febrinanto, Falih Gozi, 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)
IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting
by: Lee, Gawon, et al.
Published: (2025)
by: Lee, Gawon, et al.
Published: (2025)
Are KANs Effective for Multivariate Time Series Forecasting?
by: Han, Xiao, et al.
Published: (2024)
by: Han, Xiao, et al.
Published: (2024)
TiVaT: A Transformer with a Single Unified Mechanism for Capturing Asynchronous Dependencies in Multivariate Time Series Forecasting
by: Ha, Junwoo, et al.
Published: (2024)
by: Ha, Junwoo, et al.
Published: (2024)
Capture Timing-Attention of Events in Clinical Time Series
by: Li, Jia, et al.
Published: (2026)
by: Li, Jia, et al.
Published: (2026)
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
by: Zhao, Lifan, et al.
Published: (2024)
by: Zhao, Lifan, et al.
Published: (2024)
Nearest Neighbor Multivariate Time Series Forecasting
by: Zhang, Huiliang, et al.
Published: (2025)
by: Zhang, Huiliang, et al.
Published: (2025)
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)
Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data
by: Rewicki, Ferdinand, et al.
Published: (2025)
by: Rewicki, Ferdinand, et al.
Published: (2025)
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)
Global Cross-Time Attention Fusion for Enhanced Solar Flare Prediction from Multivariate Time Series
by: Vural, Onur, et al.
Published: (2025)
by: Vural, Onur, et al.
Published: (2025)
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions
by: Correia, Lucas, et al.
Published: (2024)
by: Correia, Lucas, et al.
Published: (2024)
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)
ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection
by: Yin, Tao, et al.
Published: (2025)
by: Yin, Tao, et al.
Published: (2025)
Similar Items
-
UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
by: Liu, Juncheng, et al.
Published: (2024) -
Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift
by: He, Hui, et al.
Published: (2024) -
Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
by: Cho, Dongchan, et al.
Published: (2025) -
Causal Disentanglement Learning for Accurate Anomaly Detection in Multivariate Time Series
by: Kim, Wonah, et al.
Published: (2025) -
Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection
by: Baumgartner, David, et al.
Published: (2026)