Copertina

Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Serramazza, Davide Italo, Nguyen, Thach Le, Ifrim, Georgiana
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification
di: Serramazza, Davide Italo, et al.
Pubblicazione: (2025)

Robust Explainer Recommendation for Time Series Classification
di: Nguyen, Thu Trang, et al.
Pubblicazione: (2023)

PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
di: Der, Audrey, et al.
Pubblicazione: (2024)

On Multivariate Financial Time Series Classification
di: Bournassenko, Grégory
Pubblicazione: (2025)

ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification
di: Le, Xuan-May, et al.
Pubblicazione: (2024)

Improving Position Encoding of Transformers for Multivariate Time Series Classification
di: Foumani, Navid Mohammadi, et al.
Pubblicazione: (2023)

The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification
di: Middlehurst, Matthew, et al.
Pubblicazione: (2026)

Time-Series Classification with Multivariate Statistical Dependence Features
di: Sun, Yao, et al.
Pubblicazione: (2026)

Graph-Aware Contrasting for Multivariate Time-Series Classification
di: Wang, Yucheng, et al.
Pubblicazione: (2023)

Univariate Channel Fusion for Multivariate Time Series Classification
di: Moro, Fernando, et al.
Pubblicazione: (2026)

Counterfactual Explainable AI (XAI) Method for Deep Learning-Based Multivariate Time Series Classification
di: Cetina, Alan G. Paredes, et al.
Pubblicazione: (2025)

Data Augmentation for Multivariate Time Series Classification: An Experimental Study
di: Ilbert, Romain, et al.
Pubblicazione: (2024)

Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification
di: Refoyo, Mario, et al.
Pubblicazione: (2024)

Navigating Time's Possibilities: Plausible Counterfactual Explanations for Multivariate Time-Series Forecast through Genetic Algorithms
di: Zuin, Gianlucca, et al.
Pubblicazione: (2026)

Learning Actionable Counterfactual Explanations in Large State Spaces
di: Naggita, Keziah, et al.
Pubblicazione: (2024)

On the Trade-offs between Adversarial Robustness and Actionable Explanations
di: Krishna, Satyapriya, et al.
Pubblicazione: (2023)

A Multi-Agent Framework for Interpreting Multivariate Physiological Time Series
di: Gabrielli, Davide, et al.
Pubblicazione: (2026)

ST-Tree with Interpretability for Multivariate Time Series Classification
di: Du, Mingsen, et al.
Pubblicazione: (2024)

What-If Explanations Over Time: Counterfactuals for Time Series Classification
di: Schlegel, Udo, et al.
Pubblicazione: (2026)

CAFO: Feature-Centric Explanation on Time Series Classification
di: Kim, Jaeho, et al.
Pubblicazione: (2024)

User-friendly Foundation Model Adapters for Multivariate Time Series Classification
di: Feofanov, Vasilii, et al.
Pubblicazione: (2024)

Wavelet Probabilistic Recurrent Convolutional Network for Multivariate Time Series Classification
di: Yang, Pu, et al.
Pubblicazione: (2025)

ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts
di: Małkus, Bartłomiej, et al.
Pubblicazione: (2025)

From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification
di: Mozolewski, Maciej, et al.
Pubblicazione: (2025)

Causal Explanation of Concept Drift -- A Truly Actionable Approach
di: Komnick, David, et al.
Pubblicazione: (2025)

TriShGAN: Enhancing Sparsity and Robustness in Multivariate Time Series Counterfactuals Explanation
di: Ma, Hongnan, et al.
Pubblicazione: (2025)

CATS: Mitigating Correlation Shift for Multivariate Time Series Classification
di: Lin, Xiao, et al.
Pubblicazione: (2025)

Counterfactual Training: Teaching Models Plausible and Actionable Explanations
di: Altmeyer, Patrick, et al.
Pubblicazione: (2026)

A Simple State Space Model Excels at Multivariate Time Series Classification
di: Saadatmand, Hassan, et al.
Pubblicazione: (2026)

CoCAI: Copula-based Conformal Anomaly Identification for Multivariate Time-Series
di: Pearson, Nicholas A., et al.
Pubblicazione: (2025)

Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
di: Huang, Qi, et al.
Pubblicazione: (2024)

Dynamic Relational Priming Improves Transformer in Multivariate Time Series
di: Lee, Hunjae, et al.
Pubblicazione: (2025)

Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder
di: Nguyen, Duy A., et al.
Pubblicazione: (2023)

M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
di: Li, Peiyu, et al.
Pubblicazione: (2024)

Accelerometer-Based Multivariate Time-Series Dataset for Calf Behavior Classification
di: Dissanayake, Oshana, et al.
Pubblicazione: (2024)

Hardware-Friendly Delayed-Feedback Reservoir for Multivariate Time-Series Classification
di: Ikeda, Sosei, et al.
Pubblicazione: (2025)

FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness
di: Fragkathoulas, Christos, et al.
Pubblicazione: (2024)

Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection
di: Le, Long Tan, et al.
Pubblicazione: (2025)

TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification
di: Liu, Huaiyuan, et al.
Pubblicazione: (2023)

Deep Distance Measurement Method for Unsupervised Multivariate Time Series Similarity Retrieval
di: Naito, Susumu, et al.
Pubblicazione: (2026)