Explaining Any Time Series Classifier

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works

On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623 · see citing works

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294 · see citing works

Explanation in artificial intelligence: Insights from the social sciences

Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works

Convolutional networks for images, speech, and time series

LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,637 · see citing works

Deep learning for time series classification: a review

Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342 · see citing works

Towards A Rigorous Science of Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works

Time series classification from scratch with deep neural networks: A strong baseline

Wang, Z., Yan, W., & Oates, T. (2017) · 2017 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966039 · Cited by 2,299 · see citing works

Untitled work

(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959 · see citing works

Adversarial Autoencoders

Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015) · arXiv · doi:10.48550/arxiv.1511.05644 · Cited by 1,579 · see citing works

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

Bagnall, A., Lines, J., Bostrom, A., Large, J., & Keogh, E. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0483-9 · Cited by 1,377 · see citing works

Time series shapelets

Ye, L., & Keogh, E. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557122 · Cited by 992 · see citing works

Interpretable Decision Sets: A Joint Framework for Description and Prediction

Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939874 · Cited by 687 · see citing works

A Survey of Methods for Explaining Black Box Models

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works

Comprehensible classification models

Freitas, A. A. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2594473.2594475 · Cited by 575 · see citing works

Towards Robust Interpretability with Self-Explaining Neural NetworksPreprint

Alvarez-Melis, D., & Alvarez-Melis, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07538 · Cited by 545 · see citing works

Learning time-series shapelets

Grabocka, J., Schilling, N., Wistuba, M., & Schmidt-Thieme, L. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623613 · Cited by 489 · see citing works

Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets

Rakthanmanon, T., & Keogh, E. (2013) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972832.74 · Cited by 408 · see citing works

Distilling a neural network into a soft decision tree

Frosst, N., & Hinton, G. (2017) · arXiv · doi:10.48550/arxiv.1711.09784 · Cited by 407 · see citing works

Time series shapelets: a novel technique that allows accurate, interpretable and fast classification

Ye, L., & Keogh, E. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0179-5 · Cited by 406 · see citing works

A shapelet transform for time series classification

Lines, J., Davis, L. M., Hills, J., & Bagnall, A. J. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339579 · Cited by 380 · see citing works

A Bag-of-Features Framework to Classify Time Series

Baydogan, M. G., Runger, G., & Tuv, E. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.72 · Cited by 361 · see citing works

Estimating the intrinsic dimension of datasets by a minimal neighborhood information

Facco, E., D’Errico, M. N., Rodríguez, Á., & Laio, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-11873-y · Cited by 360 · see citing works

Factual and Counterfactual Explanations for Black Box Decision Making

Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., & Turini, F. (2019) · IEEE Intelligent Systems · doi:10.1109/mis.2019.2957223 · Cited by 349 · see citing works

Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning

Byrne, R. M. J. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/876 · Cited by 285 · see citing works

SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model

Senin, P., & Malinchik, S. (2013) · Proceedings · doi:10.1109/icdm.2013.52 · Cited by 254 · see citing works

Prototype selection for interpretable classification

Bien, J., & Tibshirani, R. J. (2011) · The Annals of Applied Statistics · doi:10.1214/11-aoas495 · Cited by 252 · see citing works

Meaningful Explanations of Black Box AI Decision Systems

Pedreschi, D., Giannotti, F., Guidotti, R., Monreale, A., Ruggieri, S., & Turini, F. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019780 · Cited by 233 · see citing works

Towards A Rigorous Evaluation Of XAI Methods On Time Series

Schlegel, U., Arnout, H., El‐Assady, M., Oelke, D., & Keim, D. A. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00516 · Cited by 218 · see citing works

Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile

Yeh, C. M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H. A., Zimmerman, Z., Silva, D. F., Mueen, A., & Keogh, E. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0519-9 · Cited by 136 · see citing works

Data augmentation using synthetic data for time series classification with deep residual networksPreprint

Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., & Müller, P.-A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.02455 · Cited by 129 · see citing works

Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations

Nguyen, T. L., Gsponer, S., Ilie, I., O’Reilly, M., & Ifrim, G. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00633-3 · Cited by 111 · see citing works

Black Box Explanation by Learning Image Exemplars in the Latent Feature Space

Guidotti, R., Monreale, A., Matwin, S., & Pedreschi, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46150-8_12 · Cited by 79 · see citing works

Digital hand atlas and web-based bone age assessment: system design and implementation

Cao, F. F., Huang, H. K., Piętka, E., & Gilsanz, V. (2000) · Computerized Medical Imaging and Graphics · doi:10.1016/s0895-6111(00)00026-4 · Cited by 75 · see citing works

SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model

Senin, P., & Malinchik, S. (2013) · doi:10.21236/ada603196 · Cited by 65 · see citing works

Matrix Profile V

Dau, H. A., & Keogh, E. (2017) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3097993 · Cited by 60 · see citing works

On The Stability of Interpretable Models

Guidotti, R., & Ruggieri, S. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852158 · Cited by 48 · see citing works

Interpretable Categorization of Heterogeneous Time Series Data

Silbermann, J., Lee, R., Kochenderfer, M. J., & Mengshoel, O. J. (2017) · NASA STI Repository (National Aeronautics and Space Administration) · Cited by 21 · see citing works

VALMOD

Linardi, M., Zhu, Y., Palpanas, T., & Keogh, E. (2018) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3183713.3193556 · Cited by 20 · see citing works

Explaining Multi-label Black-Box Classifiers for Health Applications

Panigutti, C., Guidotti, R., Monreale, A., & Pedreschi, D. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-24409-5_9 · Cited by 19 · see citing works

Contrastive Explanations to Classification Systems Using Sparse Dictionaries

Apicella, A., Isgrò, F., Prevete, R., & Tamburrini, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30642-7_19 · Cited by 14 · see citing works

ShiftTree: An Interpretable Model-Based Approach for Time Series Classification

Hidasi, B., & Gáspár-Papanek, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23783-6_4 · Cited by 13 · see citing works

Scalable kNN Search Approximation for Time Series Data

Boubrahimi, S. F., Ma, R., Aydin, B., Hamdi, S. M., & Angryk, R. A. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8546103 · Cited by 10 · see citing works

Digital hand atlas for web-based bone age assessment: system design and implementation

Cao, F. F., Huang, H. K., Piętka, E., & Gilsanz, V. (2000) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.383062 · Cited by 7 · see citing works

Predictive Modelling of Bone Age through Classification and Regression of Bone ShapesPreprint

Bagnall, A. J., & Davis, L. M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.4781 · Cited by 2 · see citing works

Counterfactual explanations and how to find them: literature review and benchmarking

Guidotti, R. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00831-6 · Cited by 497 · see citing works

Benchmarking and survey of explanation methods for black box models

Bodria, F., Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., & Rinzivillo, S. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00933-9 · Cited by 353 · see citing works

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

Theissler, A., Spinnato, F., Schlegel, U., & Guidotti, R. (2022) · IEEE Access · doi:10.1109/access.2022.3207765 · Cited by 211 · see citing works

Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey

Ding, W., Abdel‐Basset, M., Hawash, H., & Ali, A. M. A. (2022) · Information Sciences · doi:10.1016/j.ins.2022.10.013 · Cited by 196 · see citing works

Instance-Based Counterfactual Explanations for Time Series Classification

Delaney, E. D., Greene, D., & Keane, M. T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86957-1_3 · Cited by 109 · see citing works

Fairness issues, current approaches, and challenges in machine learning models

Jui, T. D., & Rivas, P. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02083-2 · Cited by 91 · see citing works

Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging

Pahud de Mortanges, A., Luo, H., Shu, S. Z., Kamath, A., Suter, Y., Shelan, M., Pöllinger, A., & Reyes, M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01190-w · Cited by 93 · see citing works

Explainable AI for time series via Virtual Inspection Layers

Vielhaben, J., Lapuschkin, S., Montavon, G., & Samek, W. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110309 · Cited by 38 · see citing works

Temporal Rule-Based Counterfactual Explanations for Multivariate Time Series

Bahri, O., Li, P., Boubrahimi, S. F., & Hamdi, S. M. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00200 · Cited by 20 · see citing works

Glacier: guided locally constrained counterfactual explanations for time series classification

Wang, Z., Samsten, I., Miliou, I., Mochaourab, R., & Papapetrou, P. (2024) · Machine Learning · doi:10.1007/s10994-023-06502-x · Cited by 20 · see citing works

Understanding Any Time Series Classifier with a Subsequence-based Explainer

Spinnato, F., Guidotti, R., Monreale, A., Nanni, M., Pedreschi, D., & Giannotti, F. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3624480 · Cited by 19 · see citing works

Robust explainer recommendation for time series classification

Nguyen, T. T. H., Nguyen, T. L., & Ifrim, G. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-024-01045-8 · Cited by 16 · see citing works

Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems

Refoyo, M., & Luengo, D. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63800-8_1 · Cited by 12 · see citing works

Metrics and Evaluations of Time Series Explanations: An Application in Affect Computing

Fouladgar, N., Alirezaie, M., & Främling, K. (2022) · IEEE Access · doi:10.1109/access.2022.3155115 · Cited by 11 · see citing works

Matrix Profile-Based Interpretable Time Series Classifier

Guidotti, R., & D’Onofrio, M. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.699448 · Cited by 11 · see citing works

Class-Specific Explainability for Deep Time Series Classifiers

Doddaiah, R., Parvatharaju, P. S., Rundensteiner, E. A., & Hartvigsen, T. (2022) · Industrial Conference on Data Mining · doi:10.1109/icdm54844.2022.00020 · Cited by 9 · see citing works

Ad-hoc explanation for time series classification

Abanda, A., Mori, U., & Lozano, J. A. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109366 · Cited by 8 · see citing works

Post-hoc Saliency Methods Fail to Capture Latent Feature Importance in Time Series Data

Schröder, M., Zamanian, A., & Ahmidi, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39539-0_10 · Cited by 7 · see citing works

Explainable for Trustworthy AI

Giannotti, F., Naretto, F., & Bodria, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-24349-3_10 · Cited by 6 · see citing works

What about the Latent Space? The Need for Latent Feature Saliency Detection in Deep Time Series Classification

Schröder, M., Zamanian, A., & Ahmidi, N. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5020032 · Cited by 6 · see citing works

EXPHLOT: EXplainable Privacy Assessment for Human LOcation Trajectories

Naretto, F., Pellungrini, R., Rinzivillo, S., & Fadda, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45275-8_22 · Cited by 5 · see citing works

Explaining deep multi-class time series classifiers

Doddaiah, R., Parvatharaju, P. S., Rundensteiner, E. A., & Hartvigsen, T. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02073-y · Cited by 4 · see citing works

XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification

Höllig, J., Thoma, S., & Grimm, F. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00168 · Cited by 4 · see citing works

A Functionally-Grounded Benchmark Framework for XAI Methods: Insights and Foundations from a Systematic Literature Review

Canha, D., Sylvain Kubler, A., Främling, K., & Fagherazzi, G. (2025) · ACM Computing Surveys · doi:10.1145/3737445 · Cited by 5 · see citing works

State Graph Based Explanation Approach for Black-Box Time Series Model

Huang, Y., Li, C., Lu, H., Riedel, T., & Beigl, M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44070-0_8 · Cited by 4 · see citing works

A benchmarking study on process model forecasting: univariate vs. multivariate approaches

Yu, Y., Peeperkorn, J., Smedt, J. D., & Weerdt, J. D. (2025) · Process Science · doi:10.1007/s44311-025-00031-7 · Cited by 3 · see citing works

Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal

State, L., Salat, H., Rubrichi, S., & Smoreda, Z. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44067-0_6 · Cited by 3 · see citing works

slidSHAPs – sliding Shapley Values for correlation-based change detection in time series

Balestra, C., Li, B., & Müller, E. (2023) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa60987.2023.10302636 · Cited by 3 · see citing works

Optimal shapelets tree for time series interpretable classification

Bonasera, L., & Gualandi, S. (2024) · EURO Journal on Computational Optimization · doi:10.1016/j.ejco.2024.100091 · Cited by 3 · see citing works

Interpretable machine learning models for COPD ease of breathing estimation

Kok, T. T., Morales, J., Deschrijver, D., Blanco-Almazán, D., Groenendaal, W., Ruttens, D., Smeets, C. J. P. P., Mihajlović, V., Ongenae, F., & Hoecke, S. V. (2025) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-025-03285-2 · Cited by 2 · see citing works

MIPCE: Generating Multiple Patches Counterfactual-Changing Explanations for Time Series Classification

Okumura, H., & Nagao, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44223-0_19 · Cited by 2 · see citing works

Global Explanations for Multivariate time series models

Arya, V., Saha, D., Hans, S., Rajasekharan, A., & Tang, T. (2023) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3570991.3570998 · Cited by 2 · see citing works

XAIVIER: Time Series Classifier Verification with Faithful Explainable AI

Šimić, I., Singh, S., Partl, C., Veas, E. E., & Sabol, V. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640544.3645217 · Cited by 1 · see citing works

TS-NUC : Nearest Unlike Cluster Guided Generative Counterfactual Estimation for Time Series Classification

Ghosh, A., Jain, R., Parida, S., & Das, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78395-1_26 · Cited by 1 · see citing works

Mitigating Data Imbalance in Time Series Classification Based on Counterfactual Minority Samples Augmentation

Wang, L., Huang, S., Zheng, C., Liao, J., Zhu, X., Li, H., & Liu, L. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737049 · Cited by 1 · see citing works

LOMATCE: LOcal Model-Agnostic Time Series Classification Explanations

Mekonnen, E. T., Longo, L., & Dondio, P. (2025) · IEEE Access · doi:10.1109/access.2025.3625442 · Cited by 1 · see citing works

Causal Post-hoc XAI: Categorisation and Systematic Literature Review

Bjøru, A. R., Helge Langseth, A., Strümke, I., & Bach, K. (2026) · Machine Learning · doi:10.1007/s10994-026-07103-0 · Cited by 1 · see citing works

Text to Time Series Representations: Towards Interpretable Predictive Models

Poggioli, M., Spinnato, F., & Guidotti, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45275-8_16 · Cited by 1 · see citing works

Exploiting auto-encoders for explaining black-box classifiers

Guidotti, R. (2022) · Intelligenza Artificiale · doi:10.3233/ia-220139

User tests & techniques for the post-hoc explanation of deep learning

Delaney, E. D., Kenny, E. M., Greene, D., & Keane, M. T. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-396098-4.00019-3

Real-Time Defect Prevention in Continuous Casting

Hänisch, P. O., Dutta, B., Murthy, R., & Kirmse, C. (2023) · Iron & Steel Technology Conference and Exposition (AISTech) · doi:10.33313/387/250

Explaining Time Series Predictions via Relevant Shapelets

Adel, T. (2024) · International Conference on Advances in Artificial Intelligence (ICAAI) · doi:10.1145/3704137.3704190

Implet: A Post-Hoc Subsequence Explainer for Time Series Models

Meng, F., Kan, Z., Rezaei, S., Kong, Z., Chen, X., & Liu, X. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00270

From Embeddings to Exploration: Engineering Interactive Latent Space Visualizations for AI Model Sensemaking EICS012

Vanbrabant, S., Thys, J., Eerlings, G., Luyten, K., Rovelo, G., & Vanacken, D. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816764

FastPACE: Fast PlAnning of Counterfactual Explanations for time series classification

Refoyo, M., Boleas, Y., & Luengo, D. (2026) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-026-01242-7

Explaining Medical Time Series Classifiers with Abstract Features

Kok, T. T., Morales, J., Mihajlović, V., Deschrijver, D., Ongenae, F., & Hoecke, S. V. (2026) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz69877.2026.11626471

A Model Agnostic Local Explainer for Time Series Black-Box Classifiers

Spinnato, F. (2020) · Electronic Theses and Dissertations Repository (University of Pisa)

Understanding Any Time Series Classifier with a Subsequence-based Explainer

Spinnato, F., Guidotti, R., Monreale, A., Nanni, M., Pedreschi, D., & Giannotti, F. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3624480 · Cited by 19 · see citing works

Interpretable time series classification

Wang, Y. (2021) · HAL · doi:10.70675/07ed6f76z6e51z4803zb2cez6cdac7921444

Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series ClassificationPreprint

Huang, Q., Chen, W., Bäck, T., & van Stein, B. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.01343 · Cited by 2 · see citing works

timeXplain -- A Framework for Explaining the Predictions of Time Series ClassifiersPreprint

Mujkanovic, F., Doskoč, V., Schirneck, M., Schäfer, P., & Friedrich, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.07606 · Cited by 23 · see citing works