A Review of Explainable Semi–Supervised Methods in Multivariate Time Series Analysis
References
- Abdullah, T.A.A., Zahid, M.S.M., Ali, W. and Hassan, S.U. (2023). B-LIME: An improvement of LIME for interpretable deep learning classification of cardiac arrhythmia from ECG signals, Processes 11(2): 595.
- Adadi, A. and Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI), IEEE Access 6: 52138–52160.
- Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M. and Kim, B. (2020). Sanity checks for saliency maps, htt ps://arxiv.org/abs/1810.03292.
- Agrahari, S. and Singh, A.K. (2022). Concept drift detection in data stream mining: A literature review, Journal of King Saud University—Computer and Information Sci-ences 34(10): 9523–9540.
- Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J.M., Confalonieri, R., Guidotti, R., Ser, J.D., Díaz-Rodríguez, N. and Herrera, F. (2023). Explainable artificial intelligence (XAI): What we know and what is left to attain trustworthy artificial intelligence, Information Fusion 99: 101805, DOI: 10.1016/j.inffus.2023.101805.
- Atitey, K., Motsinger-Reif, A.A. and Anchang, B. (2023). Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics, Briefings in Bioinformatics 25(1), DOI: 10.1093/bib/bbad455.
- Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R. and Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Information Fusion 58: 82–115, DOI: 10.1016/j.inffus.2019.12.012.
- Bell, A., Solano-Kamaiko, I., Nov, O. and Stoyanovich, J. (2022). It’s just not that simple: An empirical study of the accuracy-explainability trade-off in machine learning for public policy, 2022 ACM Conference on Fairness Ac-countability and Transparency, Seoul, Republic of Korea, pp. 248–266.
- Bento, J., Saleiro, P., Cruz, A.F., Figueiredo, M.A. and Bizarro, P. (2021). TimeSHAP: Explaining recurrent models through sequence perturbations, Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, New York, USA, pp. 2565–2573.
- Bijlani, N., Maldonado, O.M., Nilforooshan, R., Barnaghi, P. and Kouchaki, S. (2024). Utilizing graph neural networks for adverse health detection and personalized decision making in sensor-based remote monitoring for dementia care, Computers in Biology and Medicine 183(C): 109287.
- Buçinca, Z., Lin, P., Gajos, K.Z. and Glassman, E.L. (2020). Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems, Pro-ceedings of the 25th International Conference on Intelli-gent User Interfaces, New York, USA, p. 454–464, DOI: 10.1145/3377325.3377498.
- Cai, B., Huang, G., Yang, S., Xiang, Y. and Chi, C.-H. (2023). Se-shapelets: Semi-supervised clustering of time series using representative shapelets, Expert Systems with Applications 240(C): 0957–4174, DOI: 10.1016/j.eswa.2023.122584.
- Casalino, G., Castellano, G. and Mencar, C. (2019). Data stream classification by dynamic incremental semi-supervised fuzzy clustering, International Journal on Artificial Intelligence Tools 28(08): 1960009.
- Chakrabarty, S. and Levkowitz, H. (2020). A new algorithm using independent components for classification and prediction of high dimensional data, Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Valletta, Malta, pp. 265–272.
- Cheema, P., Alamdari, M.M., Vio, G., Azizi, L. and Luo, S. (2023). On the use of matrix profiles and optimal transport theory for multivariate time series anomaly detection within structural health monitoring, Mechanical Systems and Signal Processing 204: 110797.
- Chen, C., Liu, Q., Wang, X., Liao, C. and Zhang, D. (2021). SEMI-TRAJ2GRAPH: Identifying fine-grained driving style with GPS trajectory data via multi-task learning, IEEE Transactions on Big Data 8(6): 1550–1565, DOI: 10.1109/TBDATA.2021.3063048.
- Chen, X., Gao, Y., Yu, H., Wang, H. and Cai, Y. (2023). Driving style feature extraction and recognition based on hyperdimensional computing and semi-supervised twin projection vector machine, IEEE Transactions on Intelli-gent Transportation Systems 24(12): 13976–13988.
- Costa, N., Sanchez, L. and Couso, I. (2021). Semi-supervised recurrent variational autoencoder approach for visual diagnosis of atrial fibrillation, IEEE Access 9: 40227–40239.
- Dai, S., Li, Z., Li, L., Zheng, N. and Wang, S. (2022). A flexible and explainable vehicle motion prediction and inference framework combining semi-supervised AOG and ST-LSTM, IEEE Transactions on Intelligent Transporta-tion Systems 23(2): 840–860.
- Dau, H.A., Keogh, E., Kamgar, K., Yeh, C.-C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, C.A., Yanping, Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G. and Hexagon-ML (2018). The UCR time series classification archive, https://www.cs.ucr.edu/˜eamonn/time_series_data_2018/.
- de la Peña, M.F., Ángel Luis Perales Gómez and Maimó, L.F. (2025). ShaTS: A Shapley-based explainability method for time series artificial intelligence models applied to anomaly detection in industrial Internet of things, https://arxiv.org/abs/2506.01450.
- Dembinsky, D., Lucieri, A., Frolov, S., Najjar, H., Watanabe, K. and Dengel, A. (2026). Unifying VXAI: A systematic review and framework for the evaluation of explainable AI, https://arxiv.org/abs/2506.15408.
- Di Marino, A., Bevilacqua, V., Ciaramella, A., De Falco, I. and Sannino, G. (2025). Ante-HOC methods for interpretable deep models: A survey, ACM Computing Surveys 57(10): 262:1–262:36, DOI: 10.1145/3728637.
- Doshi-Velez, F. and Kim, B. (2017). Towards a rigorous science of interpretable machine learning, https://arxiv.org/abs/1702.08608.
- Ekong, F., Yu, Y., Patamia, R.A., Sarpong, K., Ukwuoma, C.C., Ukot, A.R. and Cai, J. (2024). Retves segmentation: A pseudo-labeling and feature knowledge distillation optimization technique for retinal vessel channel enhancement, Computers in Biology and Medicine 182: 109150.
- El Amouri, H., Lampert, T., Gançarski, P. and Mallet, C. (2023). Constrained DTW preserving shapelets for explainable time-series clustering, Pattern Recognition 143(C): 109804.
- El Marhraoui, Y., Bouilland, S., Boukallel, M., Anastassova, M. and Ammi, M. (2023). CNN-based self-attention weight extraction for fall event prediction using balance test score, Sensors 23(22): 9194.
- Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C.-K. and Li, X. (2024). Label-efficient time series representation learning: A review, IEEE Transactions on Artificial Intelligence 5(12): 6027–6042.
- Ertl, B., Schneider, M., Diekmann, C., Meyer, J. and Streit, A. (2021). A Semi-supervised Approach for Trajectory Segmentation to Identify Different Moisture Processes in the Atmosphere, Springer International Publishing, Berlin/Heidelberg, pp. 264–277.
- Fernandez-Delgado, M., Cernadas, E., Barro, S. and Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems?, Journal of Machine Learn-ing Research 15(1): 3133–3181.
- Finzel, B. (2025). Current methods in explainable artificial intelligence and future prospects for integrative physiology, Pflügers Archiv—European Journal of Physiology 477(4): 513–529, DOI: 10.1007/s00424-025-03067-7.
- Flamary, R., Fauvel, M., Dalla Mura, M. and Valero, S. (2015). Analysis of multitemporal classification techniques for forecasting image time series, IEEE Geoscience and Remote Sensing Letters 12(5): 953–957.
- Fouzi, H., Ramakrishna, K.K., Madakyaru, M. and Ying, S. (2024). Efficient data-driven occupancy detection in office environments and feature impact analysis, International Journal of Information Technology 17(5): 3013–3023.
- Friedman, J., Hastie, T. and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso, Biostatis-tics 9(3): 432–441.
- Gao, Y., Tan, T., Wang, X., Beets-Tan, R., Zhang, T., Han, L., Portaluri, A., Lu, C., Liang, X., Teuwen, J., Zhou, H.-Y. and Mann, R. (2025). Multi-modal longitudinal representation learning for predicting neoadjuvant therapy response in breast cancer treatment, IEEE Journal of Biomedical and Health Informatics 29(12): 9041–9050.
- Garouani, M., Mothe, J., Barhrhouj, A. and Aligon, J. (2024). Investigating the duality of interpretability and explainability in machine learning, 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI), Herndon, USA, p. 861–867, DOI: 10.1109/ICTAI62512.2024.00125.
- Girault, B., Goncalves, P., Fleury, E. and Mor, A.S. (2014). Semi-supervised learning for graph to signal mapping: A graph signal Wiener filter interpretation, 2014 IEEE Inter-national Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, pp. 1115–1119.
- Givisis, I., Kalatzis, D., Christakis, C. and Kiouvrekis, Y. (2025). Comparing explainable AI models: SHAP, LIME, and their role in electric field strength prediction over urban areas, Electronics 14(23): 4766.
- Grzegorowski, M., Janusz, A., Marcinowski, Ł., Skowron, A., Ślezak, D. and Śliwa, G. (2025). On explainability of cluster prototypes with rough sets: A case study in the FMCG market, International Journal of Applied Mathematics and Computer Science 35(1): 19–31, DOI: 10.61822/amcs-2025-0002.
- Gu, X. (2022). An explainable semi-supervised self-organizing fuzzy inference system for streaming data classification, Information Sciences 583: 364–385.
- Guillemé, M., Masson, V., Rozé, L. and Termier, A. (2019). Agnostic local explanation for time series classification, 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA, pp. 432–439.
- Harrou, F., Kini, K.R., Madakyaru, M. and Sun, Y. (2024). A semi-supervised anomaly detection strategy for drunk driving detection: A feasibility study, Frontiers in Sensors 5: 1375034.
- Hase, P. and Bansal, M. (2020). Evaluating explainable AI: Which algorithmic explanations help users predict model behavior? https://arxiv.org/abs/2005.01831.
- Herm, L.-V., Heinrich, K., Wanner, J. and Janiesch, C. (2023). Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability, International Journal of Information Management 69(C): 102538.
- Hoffman, R.R., Mueller, S.T., Klein, G. and Litman, J. (2023). Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance, Frontiers in Computer Science 5: 1096257.
- Huang, Y., Yu, G. and Yang, Y. (2023). MIGGRI: A multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images, PLOS Computational Biology 19(11): e1011623.
- ISO, I.T. (2020). Software and systems engineering: Software testing. Part 11: Guidelines on the testing of AI-based systems, ISO/IEC TR 29119-11:2020, International Organization for Standarization, Geneva, https://www.iso.org/standard/79016.html.
- ISO, I.T. (2025). Objectives and approaches for explainability and interpretability of ML models and AI systems, International Organization for Standardization, ISO/IEC TS 6254:2025, Geneva, https://www.iso.org/standard/82148.html.
- Ivanov, P., Shtark, M., Kozhevnikov, A., Golyadkin, M., Botov, D. and Makarov, I. (2025). SensorDBSCAN: Semi-supervised active learning powered method for anomaly detection and diagnosis, IEEE Access 13: 25186–25197.
- Jullum, M., Redelmeier, A. and Aas, K. (2021). GroupShapley: Efficient prediction explanation with Shapley values for feature groups, https://arxiv.org/abs/2106.12228.
- Jutte, A., Ahmed, F., Linssen, J. and van Keulen, M. (2025). C-SHAP for time series: An approach to high-level temporal explanations, https://arxiv.org/abs/2504.11159
- Kabir, S., Hossain, M.S. and Andersson, K. (2025). A semi-supervised-learning-aided explainable belief rule-based approach to predict the energy consumption of buildings, Algorithms 18(6): 305.
- Kaczmarek-Majer, K., Casalino, G., Castellano, G., Hryniewicz, O. and Dominiak, M. (2022). Explaining smartphone-based acoustic data in bipolar disorder: SEMI-supervised fuzzy clustering and relative linguistic summaries, Information Sciences 588(C): 174–195.
- Kim, J., Maathuis, H. and Sent, D. (2024). Human-centered evaluation of explainable AI applications: A systematic review, Frontiers in Artificial Intelligence 7: 1456486.
- Krishna, S., Han, T., Gu, A., Wu, S., Jabbari, S. and Lakkaraju, H. (2022). The disagreement problem in explainable machine learning: A practitioner’s perspective, Open-Review.net, https://openreview.net/forum?id=jESY2WTZCe.
- Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017). ImageNet classification with deep convolutional neural networks, Communications of the ACM 60(6): 84–90.
- Kruschel, S., Hambauer, N., Weinzierl, S., Zilker, S., Kraus, M. and Zschech, P. (2024). Challenging the performance-interpretability trade-off: An evaluation of interpretable machine learning models, Business & Infor-mation Systems Engineering 68(1): 159–183
- Le Nguyen, T. and Ifrim, G. (2025). TSHAP: Fast and exact SHAP for explaining time series classification and regression, ECML PKDD 2025, Porto, Portugal, pp. 60–77.
- Leblanc, B. and Germain, P. (2023). On the relationship between interpretability and explainability in machine learning, DOI: 10.48550/arXiv.2311.11491.
- Leite, D., Decker, L., Santana, M. and Souza, P. (2020). EGFC: Evolving Gaussian fuzzy classifier from never-ending semi-supervised data streams with application to power quality disturbance detection and classification, 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, pp. 1–9.
- Li, C., Denison, T. and Zhu, T. (2025). A survey of few-shot learning for biomedical time series, IEEE Reviews in Biomedical Engineering 18: 192–210.
- Li, Z., Cai, R., Fu, T.Z.J., Hao, Z. and Zhang, K. (2024). Transferable time-series forecasting under causal conditional shift, IEEE Transactions on Pattern Analysis and Machine Intelligence 46(4): 1932–1949.
- Liao, Q.V., Zhang, Y., Luss, R., Doshi-Velez, F. and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI, https://arxiv.org/abs/2206.10847.
- Libbrecht, M., Hoffman, M., Bilmes, J. and Noble, W. (2015). Entropic graph-based posterior regularization, Proceed-ings of the 32nd International Conference on Machine Learning, Lille, France, pp. 1992–2000.
- Lin, S., Yurtman, A., Soenen, J. and Blockeel, H. (2025). LinC: Explaining time series clusterings with user-provided constraints, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Turin, pp. 37–52.
- Linardatos, P., Papastefanopoulos, V. and Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods, Entropy 23(1): 18.
- Lipton, Z.C. (2017). The mythos of model interpretability, htt ps://arxiv.org/abs/1606.03490.
- Lipton, Z.C. and Steinhardt, J. (2018). Troubling trends in machine learning scholarship, https://arxiv.org/abs/1807.03341.
- Liu, C., Wang, F., Hu, J. and Xiong, H. (2015). Temporal phenotyping from longitudinal electronic health records: A graph based framework, Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discov-ery and Data Mining, New York, USA, pp. 705–714.
- Liu, Z., Liu, D., Sacchi, M.D., Li, J., Chen, X., Wu, Y. and Liu, G. (2025). Physically guided high-resolution acoustic impedance inversion based on hybrid networks, IEEE Transactions on Geoscience and Remote Sensing 63: 1–10.
- Liu, Z., Pei, W., Lan, D. and Ma, Q. (2024). Diffusion language-shapelets for semi-supervised time-series classification, AAAI-24 Technical Track 38(13): 14079–14087.
- Long, B., Liu, E., Qiu, R. and Duan, Y. (2025). Explainable AI the latest advancements and new trends, https://arxiv.org/abs/2505.07005.
- Loyola-Gonzalez, O. (2019). Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view, IEEE Access 7: 154096–154113.
- Lughofer, E. (2022). Evolving multi-label fuzzy classifier, Information Sciences 597: 1–23.
- Lundberg, S. and Lee, S.-I. (2017). A unified approach to interpreting model predictions, DOI: 10.1016/j.ins.2022.03.045.
- Löfström, H., Hammar, K. and Johansson, U. (2022). A meta survey of quality evaluation criteria in explanation methods, in J. De Weerdt and A. Polyvyanyy (Eds), Intelligent Information Systems, Lecture Notes in Business Information Processing, Vol. 452, pp. 55–63, DOI: 10.1007/978-3-031-07481-37.
- Martakis, P., Movsessian, A., Reuland, Y., Pai, S.G., Quqa, S., Cava, D., Tcherniak, D. and Chatzi, E. (2022). A semi-supervised interpretable machine learning framework for sensor fault detection, Smart Structures and Systems 29(1): 251–266, DOI: 10.12989/sss.2022.29.1.251.
- Memarzadeh, M., Akbari Asanjan, A. and Matthews, B. (2022). Robust and explainable semi-supervised deep learning model for anomaly detection in aviation, Aerospace 9(8): 437.
- Meng, H., Wagner, C. and Triguero, I. (2024). SEGAL time series classification—Stable explanations using a generative model and an adaptive weighting method for LIME, Neural Networks 176(C): 106345.
- Michałowska, K., Riemer-Sørensen, S., Sterud, C. and Hjellset, O.M. (2021). Anomaly detection with unknown anomalies: Application to maritime machinery, IFAC-PapersOnLine 54(16): 105–111.
- Michelioudakis, E., Artikis, A. and Paliouras, G. (2023). Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarity, Machine Learning 113(3): 1445–1481.
- Miller, T. (2018). Explanation in artificial intelligence: Insights from the social sciences, https://arxiv.org/abs/1706.07269.
- Min, C.-H. and Tewfik, A.H. (2011). Semi-supervised event detection using higher order statistics for multidimensional time series accelerometer data, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, USA, pp. 365–368.
- Molnar, C., Casalicchio, G. and Bischl, B. (2020). Interpretable machine learning—A brief history, state-of-the-art and challenges, ECML PKDD 2020 (online), p. 417–431.
- Moradi, M., Komninos, P. and Zarouchas, D. (2024). Constructing explainable health indicators for aircraft engines by developing an interpretable neural network with discretized weights, Applied Intelligence 55(2): 143.
- Movsessian, A., Garcia Cava, D., Tcherniak, D. and Janeliukstis, R. (2020). A methodology on interpretable novelty detection, XI International Conference on Structural Dy-namics, EURODYN 2020, Athens, Greece, pp. 922–935.
- Murdoch, W.J., Singh, C., Kumbier, K., Abbasi-Asl, R. and Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning, Proceedings of the Na-tional Academy of Sciences 116(44): 22071–22080, DOI: 10.1073/pnas.1900654116.
- Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M. and Seifert, C. (2023). From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable AI, ACM Computing Surveys 55(13s): 1–42.
- Nayebi, A., Tipirneni, S., Reddy, C.K., Foreman, B. and Subbian, V. (2022). WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values, Journal of Biomedical Informatics 144: 104438, DOI: 10.1016/j.jbi.2023.104438.
- Neely, M., Schouten, S.F., Bleeker, M.J.R. and Lucic, A. (2021). Order in the court: Explainable AI methods prone to disagreement, https://arxiv.org/abs/2105.03287.
- Nguyen, H., Cao, H., Nguyen, V. and Pham, D. (2021). Evaluation of explainable artificial intelligence: SHAP, LIME, and CAM, Proceedings of the FPT AI Conference, pp: 1–6.
- Nguyen, T.T., Le Nguyen, T. and Ifrim, G. (2024). Robust explainer recommendation for time series classification, Data Mining and Knowledge Discovery 38(6): 3372–3413.
- Noah, O. and Pum, M. (2024). Evaluating explainability in AI models: Comparing SHAP, LIME, and other techniques, https://www.researchgate.net/publication/389357147.
- Parcalabescu, L. and Frank, A. (2023). On measuring faithfulness or self-consistency of natural language explanations, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand, pp. 6048–6089.
- Qin, H., Zhan, X., Li, Y., Yang, X. and Zheng, Y. (2021). Network-wide traffic states imputation using self-interested coalitional learning, Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, KDD 21, New York, USA, pp. 1370–1378.
- Quinlan, J.R. (1986). Induction of decision trees, Machine Learning 1(1): 81–106.
- Rajendran, S., Meert, W., Lenders, V. and Pollin, S. (2019). Unsupervised wireless spectrum anomaly detection with interpretable features, IEEE Transactions on Cognitive Communications and Networking 5(3): 637–647.
- Rao, S. and Wang, J. (2024). A comprehensive fault detection and diagnosis method for chemical processes, Chemical Engineering Science 300: 120565.
- Ribeiro, M. T., Singh, S. and Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 16, San Francisco, USA, pp. 1135–1144.
- Rudin, C. (2018). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nature Machine Intelligence, 1(5): 206–215.
- Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L. and Zhong, C. (2022). Interpretable machine learning: Fundamental principles and 10 grand challenges, Statistics Surveys 16: 1–85.
- Salih, A.M., Raisi-Estabragh, Z., Galazzo, I.B., Radeva, P., Petersen, S.E., Lekadir, K. and Menegaz, G. (2025). A perspective on explainable artificial intelligence methods: SHAP and LIME, Advanced Intelligent Systems 7(1): 2400304.
- Salih, A.M., Raisi-Estabragh, Z., Galazzo, I.B., Radeva, P., Petersen, S.E., Lekadir, K. and Menegaz, G. (2024). A perspective on explainable artificial intelligence methods: SHAP and LIME, Advanced Intelligent Systems 7(1): 1–8.
- Saritha, A. and Dhanalakshmi, M. (2024). A survey of advancements in anomaly detection for multivariate time series data, in C. Chakraborty et al. (Eds), Multi-faceted Approaches for Data Acquisition, Processing & Communication, Boca Raton, pp. 208–215, DOI: 10.1201/9781003470939-27.
- Schlegel, U., Arnout, H., El-Assady, M., Oelke, D. and Keim, D.A. (2019). Towards a rigorous evaluation of XAI methods on time series, 2019 ICCV Workshop on Inter-preting and Explaining Visual Artificial Intelligence Mod-els, Seoul, South Korea, pp. 4197–4201.
- Seth, P. and Sankarapu, V.K. (2025). Bridging the gap in XAI—Why reliable metrics matter for explainability and compliance, https://arxiv.org/abs/2502.04695.
- Shapley, L.S. (1953). 17. A value for n-person games, in H. W. Kuhn, and A.W. Tucker (Eds), Contributions to the Theory of Games II, Princeton University Press, Princeton, pp. 307–318.
- Sheikholeslami, S. (2019). Ablation Programming for Ma-chine Learning, Master’s thesis, KTH Royal Institute of Technology, Stockholm, p. 52.
- Shrikumar, A., Greenside, P. and Kundaje, A. (2019). Learning important features through propagating activation differences, https://arxiv.org/abs/1704.02685.
- Sivill, T. and Flach, P. (2022). LIMESegment: Meaningful, realistic time series explanations, PMLR 151: 3418–3433.
- Slack, D., Hilgard, S., Jia, E., Singh, S. and Lakkaraju, H. (2020). Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods, https://arxiv.org/abs/1911.02508.
- Sun, Y., Pang, S., Li, H., Qiao, S. and Zhang, Y. (2025). Enhanced lithology classification using an interpretable SHAP model integrating semi-supervised contrastive learning and transformer with well logging data, Natural Resources Research 34(2): 785–813.
- Trottet, C., Schürch, M., Allam, A., Barua, I., Petelytska, L., Launay, D., Airó, P., Bečvář, R., Denton, C., Radic, M., Distler, O., Hoffmann-Vold, A.-M., Krauthammer, M. and EUSTAR (2024). SEMI-supervised generative models for disease trajectories: A case study on systemic sclerosis, https://arxiv.org/abs/2407.11427.
- Turbé, H., Bjelogrlic, M., Lovis, C. and Mengaldo, G. (2023). Evaluation of POST-HOC interpretability methods in time-series classification, Nature Machine Intelligence 5(3): 250–260.
- Utkin, L.V. and Konstantinov, A.V. (2021). Ensembles of random SHAPS, https://arxiv.org/abs/2103.03302.
- Van Craenendonck, T., Dumancic, S. and Blockeel, H. (2018a). COBRA: A fast and simple method for active clustering with pairwise constraints, Proceedings of the 26th Inter-national Joint Conference on Artificial Intelligence, Mel-bourne, Australia, pp. 2871–2877.
- Van Craenendonck, T., Meert, W., Dumancic, S. and Blockeel, H. (2018b). COBRAS-TS: A new approach to semi-supervised clustering of time series, in L. Soldatova et al. (Eds), Discovery Science: DS 2018, DOI: 10.1007/978-3-030-01771-212.
- Veeraraghavan, H., Papanikolopoulos, N. and Schrater, P. (2007). Learning dynamic event descriptions in image sequences, 2007 IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos, USA, pp. 1–6.
- Vernon, E.M., Masuyama, N. and Nojima, Y. (2024). Integrating white and black box techniques for interpretable machine learning, Proceedings of 9th International Congress on Information and Communication Technology, Singapore, pp. 639–649.
- Vilone, G. and Longo, L. (2020). Explainable artificial intelligence: A systematic review, https://arxiv.org/abs/2006.00093.
- Vinzamuri, B., Khabiri, E., Bhamidipaty, A., Mckim, G. and Gandhi, B. (2020). An end-to-end context aware anomaly detection system, 2020 IEEE International Conference on Big Data (IEEE Big Data 2020), Atlanta, USA, pp. 1689–1698.
- Wang, H., Zhang, Q., Wu, J., Pan, S. and Chen, Y. (2019). Time series feature learning with labeled and unlabeled data, Pattern Recognition 89: 55–66.
- Wang, J., Zhang, R. and Li, Q. (2025a). TF-LIME: Interpretation method for time-series models based on time-frequency features, Sensors 25(9): 2845.
- Wang, X., Fang, Y., Wang, Q., Yap, P.-T., Zhu, H. and Liu, M. (2025b). Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection, Medical Image Analysis 101: 103403.
- Whiteway, M.R., Biderman, D., Friedman, Y., Dipoppa, M., Buchanan, E.K., Wu, A., Zhou, J., Bonacchi, N., Miska, N.J., Noel, J.-P., Rodriguez, E., Schartner, M., Socha, K., Urai, A.E., Salzman, C.D., Cunningham, J.P. and Paninski, L. (2021). Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders, PLOS Computational Biology 17(9): e1009439.
- Wickstrøm, K., Höhne, M. M.-C. and Hedström, A. (2024). From flexibility to manipulation: The slippery slope of XAI evaluation, https://arxiv.org/abs/2412.05592.
- Wolpert, D. and Macready, W. (1997). No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation 1(1): 67–82.
- Xie, J., Safdar, M., Chen, L., Moon, S.K. and Zhao, Y.F. (2025). Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing, Additive Manufacturing 101: 104692.
- Xu, R. and Li, Y. (2024). Interpretable spatial-temporal graph convolutional network for system log anomaly detection, Advanced Engineering Informatics 62(C): 102803.
- Ye, L. and Keogh, E. (2009). Time series shapelets: A new primitive for data mining, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discov-ery and Data Mining, KDD09, Paris, France, pp. 947–956.
- Yfantidou, S., Spathis, D., Constantinides, M., Vakali, A., Quercia, D. and Kawsar, F. (2024). Using self-supervised learning can improve model fairness, Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 24, Barcelona, Spain, pp. 3942–3953.
- Ying, R., Bourgeois, D., You, J., Zitnik, M. and Leskovec, J. (2019). GNNExplainer: Generating explanations for graph neural networks, https://arxiv.org/abs/1903.03894.
- Yuan, Y., Huang, Y., Yuan, Y. and Wang, J. (2024). SADDE: Semi-supervised anomaly detection with dependable explanations.
- Zadeh, L. (1965). Fuzzy sets, Information and Control 8(3): 338–353.
- Zhang, K., Wen, Q., Zhang, C., Cai, R., Jin, M., Liu, Y., Zhang, J.Y., Liang, Y., Pang, G., Song, D. and Pan, S. (2024). Self-supervised learning for time series analysis: Taxonomy, progress, and prospects, IEEE Transactions on Pattern Analysis and Machine Intelligence 46(10): 6775–6794.
- Zhu, L., Lu, C. and Sun, Y. (2016). Time series shapelet classification based online short-term voltage stability assessment, IEEE Transactions on Power Systems 31(2): 1430–1439.
- Zuo, J., Zeitouni, K. and Taher, Y. (2021). SMATE: Semi-supervised spatio-temporal representation learning on multivariate time series, 2021 IEEE International Con-ference on Data Mining (ICDM), Los Alamitos, USA, pp. 1565–1570.
Language: English
Page range: 235 - 266
Submitted on: Jul 31, 2025
Accepted on: Mar 30, 2026
Published on: Jun 20, 2026
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Related subjects:
© 2026 Filip Wichrowski, Marcin Ostrowski, Marta Boratyn, Katarzyna Kaczmarek-Majer, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.