Cover Image

Explanation Space: A New Perspective into Time Series Interpretability

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rezaei, Shahbaz, Liu, Xin
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
by: Kan, Ziwen, et al.
Published: (2024)

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models
by: Rezaei, Shahbaz, et al.
Published: (2025)

TimeX++: Learning Time-Series Explanations with Information Bottleneck
by: Liu, Zichuan, et al.
Published: (2024)

Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation
by: Meng, Fanyu, et al.
Published: (2024)

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

Correlating Time Series with Interpretable Convolutional Kernels
by: Chen, Xinyu, et al.
Published: (2024)

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

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

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics
by: Santamaria-Valenzuela, Inmaculada, et al.
Published: (2025)

TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
by: Jang, Hyeongwon, et al.
Published: (2025)

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

Interpretable AI for Time-Series: Multi-Model Heatmap Fusion with Global Attention and NLP-Generated Explanations
by: Francis, Jiztom Kavalakkatt, et al.
Published: (2025)

Interpretable Time Series Autoregression for Periodicity Quantification
by: Chen, Xinyu, et al.
Published: (2025)

SSET: Swapping-Sliding Explanation for Time Series Classifiers in Affect Detection
by: Fouladgar, Nazanin, et al.
Published: (2024)

L2GTX: From Local to Global Time Series Explanations
by: Mekonnen, Ephrem Tibebe, et al.
Published: (2026)

ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models
by: Huang, Bosong, et al.
Published: (2025)

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

Evaluating Simplification Algorithms for Interpretability of Time Series Classification
by: Håvardstun, Brigt, et al.
Published: (2025)

CRITS: Convolutional Rectifier for Interpretable Time Series Classification
by: Kuratomi, Alejandro, et al.
Published: (2025)

Mechanistic Interpretability for Transformer-based Time Series Classification
by: Kalnāre, Matīss, et al.
Published: (2025)

A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
by: Fatemi, Pouria, et al.
Published: (2025)

Explanation-Guided Adversarial Training for Robust and Interpretable Models
by: Chen, Chao, et al.
Published: (2026)

A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System
by: Li, Pengyu, et al.
Published: (2024)

Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining
by: Yan, Xu, et al.
Published: (2024)

CohEx: A Generalized Framework for Cohort Explanation
by: Meng, Fanyu, et al.
Published: (2024)

This Time is Different: An Observability Perspective on Time Series Foundation Models
by: Cohen, Ben, et al.
Published: (2025)

Interpreting Outliers in Time Series Data through Decoding Autoencoder
by: Knab, Patrick, et al.
Published: (2024)

Diffusion-TS: Interpretable Diffusion for General Time Series Generation
by: Yuan, Xinyu, et al.
Published: (2024)

PatchDecomp: Interpretable Patch-Based Time Series Forecasting
by: Tomioka, Hiroki, et al.
Published: (2026)

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

EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models
by: Zhou, Xinxing, et al.
Published: (2026)

Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability
by: Xu, Kunpeng, et al.
Published: (2024)

Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows
by: Chiaburu, Teodor, et al.
Published: (2024)

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

iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
by: Liang, Ziran, et al.
Published: (2025)

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

Inherently Interpretable Time Series Classification via Multiple Instance Learning
by: Early, Joseph, et al.
Published: (2023)

Interpreting Language Reward Models via Contrastive Explanations
by: Jiang, Junqi, et al.
Published: (2024)

Faithful and Stable Neuron Explanations for Trustworthy Mechanistic Interpretability
by: Yan, Ge, et al.
Published: (2025)

Causality-Aware Local Interpretable Model-Agnostic Explanations
by: Cinquini, Martina, et al.
Published: (2022)