Cover Image

ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Małkus, Bartłomiej, Bobek, Szymon, Nalepa, Grzegorz J.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Financial Management System for SMEs: Real-World Deployment of Accounts Receivable and Cash Flow Prediction
by: Małkus, Bartłomiej, et al.
Published: (2025)

Explaining Time Series Classifiers with PHAR: Rule Extraction and Fusion from Post-hoc Attributions
by: Mozolewski, Maciej, et al.
Published: (2025)

Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations
by: Bobek, Szymon, et al.
Published: (2023)

Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints
by: Bobek, Szymon, et al.
Published: (2025)

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

Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams
by: Wojak-Strzelecka, Natalia, et al.
Published: (2026)

Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution
by: Dobiczek, Karol, et al.
Published: (2026)

ProtoSSL: Interpretable Prototype Learning from Unlabeled Time-Series Data
by: Song, Steven, et al.
Published: (2026)

User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study
by: Bobek, Szymon, et al.
Published: (2024)

Dataset resulting from the user study on comprehensibility of explainable AI algorithms
by: Bobek, Szymon, et al.
Published: (2024)

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series
by: Song, Xianhao, et al.
Published: (2026)

ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting
by: Peng, Ziheng, et al.
Published: (2025)

Multi-Stage Prototype Learning for Interpretable Time Series Classification
by: Kalisetti, Bhavesh, et al.
Published: (2021)

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

On Multivariate Financial Time Series Classification
by: Bournassenko, Grégory
Published: (2025)

ProtoP-OD: Explainable Object Detection with Prototypical Parts
by: Rath-Manakidis, Pavlos, et al.
Published: (2024)

ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
by: Xiong, Guangzhi, et al.
Published: (2024)

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

ProtoTTA: Prototype-Guided Test-Time Adaptation
by: Abootorabi, Mohammad Mahdi, et al.
Published: (2026)

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

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

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

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

Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes
by: Donnelly, Jon, et al.
Published: (2021)

Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods
by: Ignatowicz, Jan, et al.
Published: (2024)

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

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

Interpretable Multivariate Time Series Forecasting Using Neural Fourier Transform
by: Koren, Noam, et al.
Published: (2024)

PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology
by: Pham, Anh-Duy, et al.
Published: (2025)

Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification
by: Serramazza, Davide Italo, et al.
Published: (2024)

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

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

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

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

EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions
by: Chakraborty, Sanjay, et al.
Published: (2024)

IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
by: Ma, Shusen, et al.
Published: (2025)

CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series
by: Lan, Tian, et al.
Published: (2025)

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

ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography
by: Ghamary, Yeganeh, et al.
Published: (2025)

ProtoSnap: Prototype Alignment for Cuneiform Signs
by: Mikulinsky, Rachel, et al.
Published: (2025)