Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection
Fuente:
arXiv
Guardado en:
| Autores principales: | Srinivasan, Abhishek, Ravi, Varun Singapuri, Andresen, Juan Carlos, Holst, Anders |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
por: González, Gastón García, et al.
Publicado: (2025)
por: González, Gastón García, et al.
Publicado: (2025)
Motif-guided Time Series Counterfactual Explanations
por: Li, Peiyu, et al.
Publicado: (2022)
por: Li, Peiyu, et al.
Publicado: (2022)
Statistical Test for Anomaly Detections by Variational Auto-Encoders
por: Miwa, Daiki, et al.
Publicado: (2024)
por: Miwa, Daiki, et al.
Publicado: (2024)
What-If Explanations Over Time: Counterfactuals for Time Series Classification
por: Schlegel, Udo, et al.
Publicado: (2026)
por: Schlegel, Udo, et al.
Publicado: (2026)
GenFacts-Generative Counterfactual Explanations for Multi-Variate Time Series
por: Seifi, Sarah, et al.
Publicado: (2025)
por: Seifi, Sarah, et al.
Publicado: (2025)
Multiple-Input Variational Auto-Encoder for Anomaly Detection in Heterogeneous Data
por: Dinh, Phai Vu, et al.
Publicado: (2025)
por: Dinh, Phai Vu, et al.
Publicado: (2025)
Counterfactual Explanations for Time Series Should be Human-Centered and Temporally Coherent in Interventions
por: Chukwu, Emmanuel C., et al.
Publicado: (2025)
por: Chukwu, Emmanuel C., et al.
Publicado: (2025)
Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
por: Huang, Qi, et al.
Publicado: (2024)
por: Huang, Qi, et al.
Publicado: (2024)
ACFormer: Mitigating Non-linearity with Auto Convolutional Encoder for Time Series Forecasting
por: Lee, Gawon, et al.
Publicado: (2026)
por: Lee, Gawon, et al.
Publicado: (2026)
AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties
por: Ji, Xiayan, et al.
Publicado: (2024)
por: Ji, Xiayan, et al.
Publicado: (2024)
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
por: Der, Audrey, et al.
Publicado: (2024)
por: Der, Audrey, et al.
Publicado: (2024)
EvoMorph: Counterfactual Explanations for Continuous Time-Series Extrinsic Regression Applied to Photoplethysmography
por: Ceylan, Mesut, et al.
Publicado: (2026)
por: Ceylan, Mesut, et al.
Publicado: (2026)
Navigating Time's Possibilities: Plausible Counterfactual Explanations for Multivariate Time-Series Forecast through Genetic Algorithms
por: Zuin, Gianlucca, et al.
Publicado: (2026)
por: Zuin, Gianlucca, et al.
Publicado: (2026)
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection
por: Huang, Tiange, et al.
Publicado: (2025)
por: Huang, Tiange, et al.
Publicado: (2025)
TriShGAN: Enhancing Sparsity and Robustness in Multivariate Time Series Counterfactuals Explanation
por: Ma, Hongnan, et al.
Publicado: (2025)
por: Ma, Hongnan, et al.
Publicado: (2025)
Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots
por: Yoo, Youngjae, et al.
Publicado: (2024)
por: Yoo, Youngjae, et al.
Publicado: (2024)
Constraint Guided AutoEncoders for Joint Optimization of Condition Indicator Estimation and Anomaly Detection in Machine Condition Monitoring
por: Meire, Maarten, et al.
Publicado: (2024)
por: Meire, Maarten, et al.
Publicado: (2024)
M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
por: Li, Peiyu, et al.
Publicado: (2024)
por: Li, Peiyu, et al.
Publicado: (2024)
On the Definition and Detection of Cherry-Picking in Counterfactual Explanations
por: Hinns, James, et al.
Publicado: (2026)
por: Hinns, James, et al.
Publicado: (2026)
Tabular Diffusion Counterfactual Explanations
por: Zhang, Wei, et al.
Publicado: (2025)
por: Zhang, Wei, et al.
Publicado: (2025)
Graph Diffusion Counterfactual Explanation
por: Bechtoldt, David, et al.
Publicado: (2025)
por: Bechtoldt, David, et al.
Publicado: (2025)
Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations
por: Ahadian, Pegah, et al.
Publicado: (2024)
por: Ahadian, Pegah, et al.
Publicado: (2024)
Physics-Guided Counterfactual Explanations for Large-Scale Multivariate Time Series: Application in Scalable and Interpretable SEP Event Prediction
por: Patil, Pranjal, et al.
Publicado: (2026)
por: Patil, Pranjal, et al.
Publicado: (2026)
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
por: Si, Haotian, et al.
Publicado: (2024)
por: Si, Haotian, et al.
Publicado: (2024)
Label-Free Multivariate Time Series Anomaly Detection
por: Zhou, Qihang, et al.
Publicado: (2023)
por: Zhou, Qihang, et al.
Publicado: (2023)
Contextual and Seasonal LSTMs for Time Series Anomaly Detection
por: Zhang, Lingpei, et al.
Publicado: (2026)
por: Zhang, Lingpei, et al.
Publicado: (2026)
Graph Anomaly Detection in Time Series: A Survey
por: Ho, Thi Kieu Khanh, et al.
Publicado: (2023)
por: Ho, Thi Kieu Khanh, et al.
Publicado: (2023)
Open-Set Multivariate Time-Series Anomaly Detection
por: Lai, Thomas, et al.
Publicado: (2023)
por: Lai, Thomas, et al.
Publicado: (2023)
TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification
por: Huang, Qi, et al.
Publicado: (2024)
por: Huang, Qi, et al.
Publicado: (2024)
A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)
por: Karami, Mohammad, et al.
Publicado: (2025)
por: Karami, Mohammad, et al.
Publicado: (2025)
Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series
por: Sadough, Amirhossein, et al.
Publicado: (2025)
por: Sadough, Amirhossein, et al.
Publicado: (2025)
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors
por: Ristea, Nicolae-Catalin, et al.
Publicado: (2023)
por: Ristea, Nicolae-Catalin, et al.
Publicado: (2023)
Discrete Graph Auto-Encoder
por: Boget, Yoann, et al.
Publicado: (2023)
por: Boget, Yoann, et al.
Publicado: (2023)
Differential Informed Auto-Encoder
por: Zhang, Jinrui
Publicado: (2024)
por: Zhang, Jinrui
Publicado: (2024)
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
por: Zhou, Yingjie, et al.
Publicado: (2026)
por: Zhou, Yingjie, et al.
Publicado: (2026)
Multivariate Time Series Anomaly Detection in Industry 5.0
por: Colombi, Lorenzo, et al.
Publicado: (2025)
por: Colombi, Lorenzo, et al.
Publicado: (2025)
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
por: Wagner, Dennis, et al.
Publicado: (2025)
por: Wagner, Dennis, et al.
Publicado: (2025)
Open Challenges in Time Series Anomaly Detection: An Industry Perspective
por: Mueller, Andreas
Publicado: (2025)
por: Mueller, Andreas
Publicado: (2025)
Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation
por: Hamon, Marine, et al.
Publicado: (2025)
por: Hamon, Marine, et al.
Publicado: (2025)
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
por: Qiu, Xiangfei, et al.
Publicado: (2025)
por: Qiu, Xiangfei, et al.
Publicado: (2025)
Ejemplares similares
-
Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
por: González, Gastón García, et al.
Publicado: (2025) -
Motif-guided Time Series Counterfactual Explanations
por: Li, Peiyu, et al.
Publicado: (2022) -
Statistical Test for Anomaly Detections by Variational Auto-Encoders
por: Miwa, Daiki, et al.
Publicado: (2024) -
What-If Explanations Over Time: Counterfactuals for Time Series Classification
por: Schlegel, Udo, et al.
Publicado: (2026) -
GenFacts-Generative Counterfactual Explanations for Multi-Variate Time Series
por: Seifi, Sarah, et al.
Publicado: (2025)