Interpretable Time-Series Classification

Latest papers

16 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series
Oct 13, 2026Maximilian Kirsch, Christine PreisachInterpretable Time-Series Classification Tree Species Classification
Intelligent Systems Research Group (ISRG), Karlsruhe University of Applied Sciences, Germany
Sea-Net: A Small and Inherently Interpretable Neural Network for Time Series Classification
Oct 13, 2026Ubaid Ur Rehman, Tanmoy MondalInterpretable Time-Series Classification Time Series Classification
FOX Team, Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) University of Lille, CNRS, Centrale Lille, UMR 9189 F-59000 Lille, France
A Time-Aware Bag-of-Receptive-Fields for Interpretable Irregular Time Series Classification
Sep 30, 2026Francesco SpinnatoIrregular Time-Series Modeling Time Series Classification
Department of Computer Science, University of Pisa, Italy · ISTI-CNR, Pisa, Italy
Learnable Time-Frequency Masks for Explaining Time-Series Classifiers
Sep 24, 2026Theresa Dahl Frehr, Francisco Pelayo, Lukas Raad +3Feature Attribution Time Series Classification
Department of Applied Mathematics and Computer Science Technical University of Denmark
Software Frameworks for Explainable AI in Time Series Classification: A Systematic Review
Aug 19, 2026Louis Peter, Nils Gumpfer, Jana Fischer +2Interpretable Time-Series Classification Explainable Artificial Intelligence
Technische Hochschule Mittelhessen - University of Applied Science, Friedberg, Germany · Hessian Center for AI (hessian.AI), Darmstadt, Germany · Marburg University, Marburg, Germany
Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity
Jul 23, 2026Hongnan Ma, Yiwei Shi, Mengyue Yang +1Counterfactual Explanations Time Series Classification
School of Computer Science, University of Bristol · School of Engineering Mathematics and Technology, University of Bristol
Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification
Jun 10, 2026Veerendhra Kumar Dangeti, Xiao Gu, Ying Weng +1Time Series Classification Training Data Selection
School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, United Kingdom · Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom · School of Computer Science, University of Nottingham Ningbo China, Ningbo, 315100, China
AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE
Jun 2, 2026Tao Xie, Zexi Tan, Haoyi Xiao +5Explainable Time Series Forecasting Multivariate Time Series Classification
Guangdong University of Technology · Guangzhou, China · Shenzhen University +7
Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series
May 21, 2026Xianhao Song, Yuang Zhang, Yuqi She +2Prototype-Based Classification Multivariate Time Series Classification
East China Normal University · Shanghai Jiao Tong University
INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification
May 19, 2026Seongjun Lee, Seokhyun Lee, Changhee LeeTime Series Classification Interpretable ML
Department of Artificial Intelligence, Korea University
What-If Explanations Over Time: Counterfactuals for Time Series Classification
Mar 29, 2026Udo Schlegel, Thomas SeidlGraph Counterfactual Explanations Counterfactual Explanations
LMU Munich, Germany · Munich Center for Machine Learning (MCML), Germany
MIHT: A Hoeffding Tree for Time Series Classification using Multiple Instance Learning
Mar 23, 2026Aurora Esteban, Amelia Zafra, Sebastián VenturaInterpretable Time-Series Classification Time Series Classification
Dept. of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, University of Cordoba, Spain
Interpretable Classification of Time Series Using Euler Characteristic Surfaces
Mar 16, 2026Salam Rabindrajit Luwang, Sushovan Majhi, Vishal Mandal +3Interpretable Time-Series Classification Time Series Classification
National Institute of Technology, Department of Physics, Ravangla, 737139, India · George Washington University, Data science program, Washington, DC, 20052, USA · Montana Technological University, Department of Mathematical Sciences, Butte, 59701, USA
L2GTX: From Local to Global Time Series Explanations
Mar 13, 2026Ephrem Tibebe Mekonnen, Luca Longo, Lucas Rizzo +1Explainable Artificial Intelligence Interpretable Time-Series Classification
School of Computer Science, Technological University Dublin Dublin, Ireland · Artificial Intelligence and Cognitive Load Research Lab, University College Cork Cork, Ireland · School of Computer Science and Information Technology, University College Cork Cork, Ireland +1
xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth
Mar 6, 2026Gregor BaerSynthetic Benchmark Generation Interpretable Time-Series Classification
Information Systems, Industrial Engineering & Innovation Sciences Eindhoven University of Technology Eindhoven, The Netherlands
Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields
Nov 29, 2023Francesco Spinnato, Riccardo Guidotti, Anna Monreale +1Time Series Classification Interpretable ML
Scuola Normale Superiore, Pisa, Italy · University of Pisa, Pisa, Italy · ISTI-CNR, Pisa, Italy

Oct 13, 2026J/K move · Enter open · S save

Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series

Maximilian Kirsch, Christine Preisach

Intelligent Systems Research Group (ISRG), Karlsruhe University of Applied Sciences, Germany

Interpretable Time-Series Classification Tree Species Classification Land Cover Classification Time Series Classification Recurrent Neural Networks

Open paper AskPDF arXiv

The ecological insurance theory posits that diverse forests are more resilient and better able to adapt to changing environmental conditions. Increasing drought and heat stress in Central Europe promotes mixed forests for maintaining ecosystem services. However, a diverse species distribution complicates remote sensing-based species mapping, as distinguishing pure from mixed species compositions at the pixel level remains challenging. We evaluated the potential of recurrent neural networks (RNNs) for tree-species classification from Sentinel-2 time series and investigated the spectral-temporal features underlying class discrimination, with particular emphasis on separating pure from mixed Norway spruce pixels. We compared RNN classification models with Random Forest (RF) and Extreme Gradient Boosting (XGBoost) and used Integrated Gradients (IG) to quantify feature attributions across spectral bands and time. The best-performing RNN achieved a Macro-F1 score of 0.851 on the test data, outperforming XGBoost by 0.012 and Random Forest by 0.103. Beyond classification performance, the attribution analysis revealed distinct spectral-temporal patterns associated with the separation of pure and mixed Norway spruce pixels. Attribution values increased particularly after approximately day of year (DOY) 125, while B11 showed the strongest contribution during periods of increasing spectral divergence between the two classes. These findings show that recurrent models can provide competitive classification performance while also revealing the phenological periods and spectral regions that are most relevant for distinguishing closely related forest compositions. Combining time-series classification with explainability therefore provides additional insight into the spectral-temporal mechanisms underlying tree-species discrimination and may support more detailed mapping of mixed forest structures.