Buchumschlag

Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained Intra- and Inter-Variate Dependencies

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Xie, Yongzheng, Zhang, Hongyu, Babar, Muhammad Ali
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Machine Learning Artificial Intelligence
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
von: Liu, Juncheng, et al.
Veröffentlicht: (2024)

Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift
von: He, Hui, et al.
Veröffentlicht: (2024)

Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
von: Cho, Dongchan, et al.
Veröffentlicht: (2025)

Causal Disentanglement Learning for Accurate Anomaly Detection in Multivariate Time Series
von: Kim, Wonah, et al.
Veröffentlicht: (2025)

Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection
von: Baumgartner, David, et al.
Veröffentlicht: (2026)

Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection
von: Kim, HyunGi, et al.
Veröffentlicht: (2025)

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

DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
von: Darban, Zahra Zamanzadeh, et al.
Veröffentlicht: (2024)

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

MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection
von: Liu, Jinyang, et al.
Veröffentlicht: (2024)

Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis
von: Liu, Zhe, et al.
Veröffentlicht: (2024)

mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
von: Zhou, Xiaona, et al.
Veröffentlicht: (2025)

Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection
von: Huang, Xiaoyu, et al.
Veröffentlicht: (2024)

CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift
von: Kim, HyunGi, et al.
Veröffentlicht: (2026)

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

Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
von: Yu, Guoqi, et al.
Veröffentlicht: (2024)

Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series
von: Chen, Kuan-Cheng, et al.
Veröffentlicht: (2025)

SEED: Spectral Entropy-Guided Evaluation of SpatialTemporal Dependencies for Multivariate Time Series Forecasting
von: Xiong, Feng, et al.
Veröffentlicht: (2025)

DARTs: A Dual-Path Robust Framework for Anomaly Detection in High-Dimensional Multivariate Time Series
von: Liu, Xuechun, et al.
Veröffentlicht: (2025)

Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approach
von: Golchin, Bahareh, et al.
Veröffentlicht: (2025)

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

VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting
von: Zhang, Xudong, et al.
Veröffentlicht: (2026)

Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction
von: Hassan, Atif, et al.
Veröffentlicht: (2025)

Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting
von: Wang, Muyao, et al.
Veröffentlicht: (2024)

Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors
von: Gallon, R., et al.
Veröffentlicht: (2024)

Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
von: Febrinanto, Falih Gozi, et al.
Veröffentlicht: (2023)

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

IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting
von: Lee, Gawon, et al.
Veröffentlicht: (2025)

Are KANs Effective for Multivariate Time Series Forecasting?
von: Han, Xiao, et al.
Veröffentlicht: (2024)

TiVaT: A Transformer with a Single Unified Mechanism for Capturing Asynchronous Dependencies in Multivariate Time Series Forecasting
von: Ha, Junwoo, et al.
Veröffentlicht: (2024)

Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
von: Zhao, Lifan, et al.
Veröffentlicht: (2024)

Capture Timing-Attention of Events in Clinical Time Series
von: Li, Jia, et al.
Veröffentlicht: (2026)

Nearest Neighbor Multivariate Time Series Forecasting
von: Zhang, Huiliang, et al.
Veröffentlicht: (2025)

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

Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data
von: Rewicki, Ferdinand, et al.
Veröffentlicht: (2025)

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

Global Cross-Time Attention Fusion for Enhanced Solar Flare Prediction from Multivariate Time Series
von: Vural, Onur, et al.
Veröffentlicht: (2025)

Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions
von: Correia, Lucas, et al.
Veröffentlicht: (2024)

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

ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection
von: Yin, Tao, et al.
Veröffentlicht: (2025)