Deep Neural Network Ensembles for Time Series Classification

ImageNet classification with deep convolutional neural networks

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ImageNet Large Scale Visual Recognition Challenge

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Deep learning for time series classification: a review

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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

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

10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH

Yang, Q., & Wu, X. (2006) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622006002258 · Cited by 939 · see citing works

Convolutional neural networks for time series classification

College of Electronic Science and Engineering, N. U. O. D. T., Zhao, B., Lu, H., College of Electronic Science and Engineering, N. U. O. D. T., Chen, S., College of Electronic Science and Engineering, N. U. O. D. T., Liu, J., College of Electronic Science and Engineering, N. U. O. D. T., Wu, D., & College of Electronic Science and Engineering, N. U. O. D. T. (2017) · Journal of Systems Engineering and Electronics · doi:10.21629/jsee.2017.01.18 · Cited by 888 · see citing works

The Loss Surfaces of Multilayer Networks

Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B., & LeCun, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.0233 · Cited by 733 · see citing works

Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks

Zheng, Y., Liu, Q., Chen, E., Ge, Y., & Zhao, J. L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08010-9_33 · Cited by 689 · see citing works

Multi-Scale Convolutional Neural Networks for Time Series ClassificationPreprint

Cui, Z.-C., Chen, W.-L., & Chen, Y. (2016) · arXiv.org · doi:10.48550/arxiv.1603.06995 · Cited by 637 · see citing works

Time series classification with ensembles of elastic distance measures

Lines, J., & Bagnall, A. J. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0361-2 · Cited by 513 · 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

Using dynamic time warping distances as features for improved time series classification

Kate, R. J. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-015-0418-x · Cited by 347 · see citing works

Data Augmentation for Time Series Classification using Convolutional Neural Networks

Guennec, A. L., Malinowski, S., & Tavenard, R. (2016) · HAL · Cited by 341 · see citing works

Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles

Lines, J., Taylor, S., & Bagnall, A. (2018) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3182382 · Cited by 333 · see citing works

Transfer learning for time series classification

Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8621990 · Cited by 319 · see citing works

Should we really use post-hoc tests based on mean-ranks?Preprint

BenavoliAlessio, CoraniGiorgio, & MangiliFrancesca (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.02288 · Cited by 318 · see citing works

Proximity Forest: an effective and scalable distance-based classifier for time series

Lucas, B., Shifaz, A., Pelletier, C., O’Neill, L., Zaidi, N., Goethals, B., Petitjean, F., & Webb, G. I. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00617-3 · Cited by 183 · see citing works

Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition

Wen, G., Hou, Z., Li, H., Li, D., Jiang, L., & Xun, E. (2017) · Cognitive Computation · doi:10.1007/s12559-017-9472-6 · Cited by 164 · see citing works

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., & Müller, P.-A. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851936 · Cited by 158 · see citing works

Proximity Forest: an effective and scalable distance-based classifier for time series

Lucas, B., Shifaz, A., Pelletier, C., O’Neill, L., Zaidi, N. A., (15827960), B. G., Petitjean, F., & (14480133), G. W. (2024) · Figshare · Cited by 147 · 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

Evaluating Surgical Skills from Kinematic Data Using Convolutional Neural Networks

Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., & Müller, P.-A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_25 · Cited by 121 · see citing works

Towards a Universal Neural Network Encoder for Time Series

Joan, S., Santiago, P., & Karatzoglou, A. (2018) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-918-8-120 · Cited by 119 · see citing works

Towards a universal neural network encoder for time seriesPreprint

Serrà, J., Pascual, S., & Karatzoglou, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.03908 · Cited by 92 · see citing works

Binary Shapelet Transform for Multiclass Time Series Classification

Bostrom, A. G., & Bagnall, A. J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22729-0_20 · Cited by 80 · see citing works

Time Series Classification Using Time Warping Invariant Echo State Networks

Tanisaro, P., & Heidemann, G. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0149 · Cited by 79 · see citing works

Time-series classification with COTE: The collective of transformation-based ensembles

Bagnall, A. J., Lines, J., Hills, J., & Bostrom, A. G. (2016) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2016.7498418 · Cited by 63 · see citing works

Ensemble Deep Learning for Biomedical Time Series Classification

Jin, L.-P., & Dong, J. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/6212684 · Cited by 60 · see citing works

Deep Dilated Convolution on Multimodality Time Series for Human Activity Recognition

Xi, R., Hou, M., Fu, M., Qu, H., & Liu, D. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489540 · Cited by 57 · see citing works

An ultra-fast time series distance measure to allow data mining in more complex real-world deployments

Gharghabi, S., Imani, S., Bagnall, A. J., Darvishzadeh, A., & Keogh, E. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00695-8 · Cited by 34 · see citing works

Text-To-Text Generative Adversarial Networks

Li, C., Su, Y., & Liu, W. (2018) · 2018 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489624 · Cited by 33 · see citing works

Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series

Dau, H. A., Silva, D. F., Petitjean, F., Forestier, G., Bagnall, A. J., & Keogh, E. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258009 · Cited by 26 · see citing works

MNRD: A Merged Neural Model For Rumor Detection In Social Media

Xu, N., Chen, G., & Mao, W. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489582 · Cited by 24 · see citing works

Predicting Bucket-Filling Control Actions of a Wheel-Loader Operator Using a Neural Network Ensemble

Dadhich, S., Sandin, F., & Bodin, U. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489388 · Cited by 20 · see citing works

Time Series Trend Detection and Forecasting Using Complex Network Topology Analysis

Anghinoni, L., Zhao, L., Zheng, Q., & Zhang, J. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489167 · Cited by 15 · see citing works

Semi-Supervised Clustering With Multiresolution Autoencoders

Ienco, D., & Pensa, R. G. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489353 · Cited by 12 · see citing works

Ensemble of binary tree structured deep convolutional network for image classification

Lee, J.-E., Min-Joo, K., & Kang, J. (2017) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2017.8282260 · Cited by 8 · see citing works

Deep neural network ensemble by data augmentation and bagging for skin lesion classificationPreprint

Goyal, M., & Rajapakse, J. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.05496 · Cited by 7 · see citing works

Hierarchical Tree Long Short-Term Memory for Sentence Representations

Wang, X., Li, C., & Xu, B. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489082 · Cited by 4 · see citing works

Multi ROI and Multi Map Networks for Accurate and Efficient Pedestrian Detection

Qiu, Z., & Gu, X. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489310 · Cited by 3 · see citing works

Stochastic Multiple Choice Learning for Acoustic Modeling

Liul, B., Nie, S., Liang, S., Yang, Z., & Liu, W. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489454 · Cited by 3 · see citing works

Inceptiontime: finding alexnet for time series classification

Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., Webb, G. I., Idoumghar, L., Muller, P.-A., & Petitjean, F. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00710-y · Cited by 1,788 · see citing works

ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels

Dempster, A., Petitjean, F., & Webb, G. I. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00701-z · Cited by 1,080 · see citing works

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Foumani, N. M., Miller, L., Tan, C. W., Webb, G. I., Forestier, G., & Salehi, M. (2024) · ACM Computing Surveys · doi:10.1145/3649448 · Cited by 296 · see citing works

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., & Müller, P.-A. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851936 · Cited by 158 · see citing works

Deep Learning for Satellite Image Time-Series Analysis: A review

Miller, L., Pelletier, C., & Webb, G. I. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3393010 · Cited by 109 · see citing works

Gamma function based ensemble of CNN models for breast cancer detection in histopathology images

Majumdar, S., Pramanik, P., & Sarkar, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119022 · Cited by 105 · see citing works

Automatic speaker verification systems and spoof detection techniques: review and analysis

Mittal, A., & Dua, M. (2021) · International Journal of Speech Technology · doi:10.1007/s10772-021-09876-2 · Cited by 69 · see citing works

Time Series Classification: A Review of Algorithms and Implementations

Faouzi, J. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.1004810 · Cited by 70 · see citing works

Adversarial Examples in Deep Learning for Multivariate Time Series Regression

Mode, G. R., & Hoque, K. A. (2020) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr50011.2020.9425190 · Cited by 65 · see citing works

Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials

Guo, F., Li, W., Jiang, P., Chen, F., & Liu, Y. (2022) · Materials · doi:10.3390/ma15124270 · Cited by 62 · see citing works

Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network

Khan, S. D., Alarabi, L., & Basalamah, S. M. (2020) · Computers · doi:10.3390/computers9040099 · Cited by 60 · see citing works

Convolutional neural network and long short-term memory models for ice-jam predictions

Madaeni, F., Chokmani, K., Lhissou, R., Homayouni​​​​​​​, S., Gauthier, Y., & Tolszczuk-Leclerc, S. (2022) · The Cryosphere · doi:10.5194/tc-16-1447-2022 · Cited by 51 · see citing works

A review of time-series analysis for cyber security analytics: from intrusion detection to attack prediction

Landauer, M., Skopik, F., Stojanović, B., Flatscher, A., & Ullrich, T. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00921-0 · Cited by 61 · see citing works

LITE: Light Inception with boosTing tEchniques for Time Series Classification

Ismail-Fawaz, A., Devanne, M., Berretti, S., Weber, J., & Forestier, G. (2023) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa60987.2023.10302569 · Cited by 42 · see citing works

Adversarial attacks and defenses in physiological computing: a systematic review

Wu, D., Xu, J., Fang, W., Zhang, Y., Yang, L., Xu, X., Luo, H., & Yu, X. M. (2022) · National Science Open · doi:10.1360/nso/20220023 · Cited by 36 · see citing works

Look into the LITE in deep learning for time series classification

Ismail-Fawaz, A., Devanne, M., Berretti, S., Weber, J., & Forestier, G. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00708-5 · Cited by 35 · see citing works

IENet: a robust convolutional neural network for EEG based brain-computer interfaces

Du, Y., & Liu, J. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac7257 · Cited by 34 · see citing works

Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis

Dong, C., Zheng, L., & Chen, W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0840-9_24 · Cited by 35 · see citing works

Neural Architecture Search for Time Series Classification

Rakhshani, H., Fawaz, H. I., Idoumghar, L., Forestier, G., Lepagnot, J., Weber, J., Brévilliers, M., & Müller, P.-A. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206721 · Cited by 30 · see citing works

Generative adversarial networks for unbalanced fetal heart rate signal classification

Puspitasari, R. D. I., Ma’Sum, M. A., Alhamidi, M. R., Kurnianingsih, K., & Jatmiko, W. (2021) · ICT Express · doi:10.1016/j.icte.2021.06.007 · Cited by 29 · see citing works

Multi-modal temporal CNNs for live fuel moisture content estimation

Miller, L., Zhu, L., Yebra, M., Rüdiger, C., & Webb, G. I. (2022) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2022.105467 · Cited by 24 · see citing works

Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions

Xu, Z., Wang, Y.-J., Feng, X., Wang, Y., Li, Y., & Lin, H. (2024) · Finance research letters · doi:10.1016/j.frl.2024.105840 · Cited by 26 · see citing works

Multi-modal lifelog data fusion for improved human activity recognition: A hybrid approach

Oh, Y., & Kim, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102464 · Cited by 22 · see citing works

Towards scalable and reusable predictive models for cyber twins in manufacturing systems

Giannetti, C., & Essien, A. E. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01804-0 · Cited by 22 · see citing works

Predicting Visual Search Task Success from Eye Gaze Data as a Basis for User-Adaptive Information Visualization Systems

Spiller, M., Liu, Y., Hossain, M. Z., Gedeon, T., Geissler, J., & Nürnberger, A. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3446638 · Cited by 20 · see citing works

Explainable Predictive Quality Inspection using Deep Learning in Electronics Manufacturing

Saadallah, A., Büscher, J., Abdulaaty, O., Panusch, T., Deuse, J., & Morik, K. J. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.05.031 · Cited by 19 · see citing works

Remote Sensing Time Series Classification Based on Self-Attention Mechanism and Time Sequence Enhancement

Liu, J., Yan, J., Wang, L., Huang, L., He, H., & Liu, H. (2021) · Remote Sensing · doi:10.3390/rs13091804 · Cited by 17 · see citing works

Time Series Classification Based on Multi-Dimensional Feature Fusion

Quan, S., Sun, M., Zeng, X., Wang, X., & Zhu, Z. (2023) · IEEE Access · doi:10.1109/access.2023.3241013 · Cited by 15 · see citing works

A Comprehensive Survey on Wi-Fi Sensing for Human Identity Recognition

Duan, P., Diao, X., Cao, Y., Zhang, D., Zhang, B., & Kong, J. (2023) · Electronics · doi:10.3390/electronics12234858 · Cited by 15 · see citing works

Image classification using convolutional neural network tree ensembles

Hafiz, A. M., Bhat, R., & Hassaballah, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13604-6 · Cited by 15 · see citing works

An Algorithm Based on Transformer and Temporal Convolution Network for Early Identification of Ternary Gas Mixture

Yang, G., Song, R., Wu, Y., Yu, J., Zhang, J., & Zhu, H. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3302790 · Cited by 14 · see citing works

T-MAN: a neural ensemble approach for person re-identification using spatio-temporal information

Tagore, N. K., Chattopadhyay, P., & Wang, L. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09398-0 · Cited by 14 · see citing works

A Hybrid Feature Extraction Method for Heart Disease Classification using ECG Signals

Akcin, E., Isleyen, K. S., Ozcan, E., Hameed, A. A., Alimovski, E., & Jamıl, A. (2021) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu52992.2021.9599070 · Cited by 14 · see citing works

Land cover multiclass classification of wonosobo, Indonesia with time series-based one-dimensional deep learning model

Sencaki, D. B., Putri, M. N., Santosa, B. H., Arfah, S., Arifandri, R., Afifuddin, A., Habibie, M. I., Putra, P. K., Anatoly, N., Permata, Z. D. O., et al. (2023) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2023.101040 · Cited by 13 · see citing works

Convolutional- and Deep Learning-Based Techniques for Time Series Ordinal Classification

Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P. A., Bagnall, A. J., & Hervás‐Martínez, C. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3498100 · Cited by 14 · see citing works

Intelligent Guidance and Control Methods for Missile Swarm

Huang, H., Zhao, X. M., & Zhang, X. Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8235148 · Cited by 13 · see citing works

A deep learning-based framework for predicting pork preference

Ko, E., Jeong, K., Oh, H., Park, Y., Choi, J., & Lee, E. (2023) · Current Research in Food Science · doi:10.1016/j.crfs.2023.100495 · Cited by 12 · see citing works

Semi-Supervised Learning for Cancer Detection of Lymph Node MetastasesPreprint

Jaiswal, A. K., Panshin, I., Shulkin, D., Aneja, N., & Abramov, S. (2019) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.5281/zenodo.3251086 · Cited by 32 · see citing works

Advancing EEG prediction with deep learning and uncertainty estimation

Tveter, M., Tveitstøl, T., Hatlestad‐Hall, C., T, A. S. P., Taubøll, E., Yazidi, A., Hammer, H. L., & Haraldsen, I. (2024) · Brain Informatics · doi:10.1186/s40708-024-00239-6 · Cited by 12 · see citing works

Deep Network Ensemble Learning applied to Image Classification using CNN TreesPreprint

Hafiz, A. M., & Bhat, G. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00829 · Cited by 11 · see citing works

Establishment of super sonic inlet flow pattern monitoring system: A workflow

Wang, Y., Han, Z.-C., Zhao, Y.-P., Wu, H., Tan, H., Zhang, Y., & Li, Y. (2021) · Aerospace Science and Technology · doi:10.1016/j.ast.2021.107297 · Cited by 10 · see citing works

Anomaly Detection for Time Series Data Stream

Wang, Q., Yan, B., Su, H.-Y., & Zheng, H. (2021) · IEEE International Conference on Big Data Analytics (ICBDA) · doi:10.1109/icbda51983.2021.9402957 · Cited by 10 · see citing works

Digit Image Recognition Using an Ensemble of One-Versus-All Deep Network Classifiers

Hafiz, A. M., & Hassaballah, M. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0882-7_38 · Cited by 10 · see citing works

Attacks Against Artificial Intelligence Systems: Classification, The Threat Model and the Approach to Protection

Kotenko, I. V., Saenko, I. B., Lauta, O., Vasiliev, N., & Kribel, K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-19620-1_28 · Cited by 8 · see citing works

Residual Attention Net for Superior Cross-Domain Time Sequence ModelingPreprint

Huang, S. H., Xu, L., & Jiang, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.04077 · Cited by 9 · see citing works

Performance comparison of single and ensemble CNN, LSTM and traditional ANN models for short-term electricity load forecasting

Kathirgamanathan, A., Patel, A., Khwaja, A. S., Venkatesh, B., & Anpalagan, A. (2022) · doi:10.32920/21408579 · Cited by 8 · see citing works

COCALITE: A Hybrid Model COmbining CAtch22 and LITE for Time Series Classification

Badi, O., Devanne, M., Ismail-Fawaz, A., Abdullayev, J., Lemaire, V., Berretti, S., Weber, J., & Forestier, G. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825872 · Cited by 7 · see citing works

Unsupervised Anomaly Detection Approach for Multivariate Time Series

Zhou, Y., Song, Y., & Qian, M. (2021) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c55045.2021.00042 · Cited by 7 · see citing works

Universal Distributional Decision-Based Black-Box Adversarial Attack with Reinforcement Learning

Huang, Y., Zhou, Y., Hefenbrock, M., Riedel, T., Fang, L., & Beigl, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30111-7_18 · Cited by 7 · see citing works

Attack based on data: a novel perspective to attack sensitive points directly

Ge, Y., Yang, Z., Chen, L., Wang, Y., & Li, C. (2023) · Cybersecurity · doi:10.1186/s42400-023-00179-4 · Cited by 6 · see citing works

Deep Neural Network Ensembles Using Class-vs-Class Weighting

Fabricius, R., Šuch, O., & Tarábek, P. (2023) · IEEE Access · doi:10.1109/access.2023.3298057 · Cited by 6 · see citing works

Probabilistic SAX: A Cognitively-Inspired Method for Time Series Classification in Cognitive IoT Sensor Network

Jha, V., & Tripathi, P. (2024) · Mobile Networks and Applications · doi:10.1007/s11036-024-02322-y · Cited by 5 · see citing works

Multi-Scale Ensemble Booster for Improving Existing TSD Classifiers

Fan, L., Chai, Y., & Chen, X. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3230709 · Cited by 5 · see citing works

Performance Analysis of Ensemble Deep Learning NARX System for Estimating the Earthquake Occurrences in the Subduction Zone of Java Island

Nugroho, H. A., Subiantoro, A., & Kusumoputro, B. (2023) · International Journal of Technology · doi:10.14716/ijtech.v14i7.6702 · Cited by 5 · see citing works

Info-CELS: Informative Saliency Map-Guided Counterfactual Explanation for Time Series Classification

Li, P., Bahri, O., Hosseinzadeh, P., Boubrahimi, S. F., & Hamdi, S. M. (2025) · Electronics · doi:10.3390/electronics14071311 · Cited by 5 · see citing works

Microphone based Smartphone enabled Spirometry Data Augmentation using Information Maximizing Generative Adversarial Network

Trivedy, S., Goyal, M. K., & Mukherjee, A. (2020) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc43012.2020.9129308 · Cited by 4 · see citing works

Consumer Behaviour in Retail: Next Logical Purchase using Deep Neural NetworkPreprint

Verma, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06952 · Cited by 4 · see citing works

Communication network security situation analysis based on time series data mining technology

Jiang, Q. (2024) · Open Computer Science · doi:10.1515/comp-2023-0104 · Cited by 3 · see citing works

Visual Intention Classification by Deep Learning for Gaze-based Human-Robot Interaction

Shi, L., Copot, C., & Vanlanduit, S. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.04.168 · Cited by 3 · see citing works

Shapelet Discovery by Lazy Time Series Classification

Zhang, W., Wang, Z., Yuan, J., & Hao, S. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/1978310 · Cited by 3 · see citing works

ACKEM: Automatic Classification, Using KNN Based Ensemble Modeling

Zagagy, B., Herman, M., & Levi, O. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-73103-8_38 · Cited by 3 · see citing works

The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing

Hernandez-Cruz, N., Nugent, C., Zhang, S., & McChesney, I. (2021) · Applied Sciences · doi:10.3390/app11167660 · Cited by 3 · see citing works

Ensemble classification of video-recorded crowd movements

Bendali-Braham, M., Weber, J., Forestier, G., Idoumghar, L., & Müller, P.-A. (2021) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa52656.2021.9552129 · Cited by 3 · see citing works

Multiclass Classification with an Ensemble of Binary Classification Deep Networks.Preprint

Hafiz, A. M., & Bhat, G. M. (2020) · arXiv (Cornell University) · Cited by 3 · see citing works

Semi-supervised Time Series Classification Through Image Representations

Rozin, B., Bergamim, E., Pedronette, D. C. G., & Breve, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36808-0_4 · Cited by 2 · see citing works

Experimental Verification of Neural Network-Based Fault Types Classifier for Current Sensors in Induction Motor Drive

Teler, K., Skowron, M., & Orłowska-Kowalska, T. (2023) · International Conference on Electrical Drives and Power Electronics (EDPE) · doi:10.1109/edpe58625.2023.10274051 · Cited by 2 · see citing works

On the Impact of Model Tolerance in Power Grid Anomaly Detection Systems

Madabhushi, S., & Dewri, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23690-7_13 · Cited by 2 · see citing works

Improving Time-Series Classification Accuracy Based on Temporal Feature Representation Learning Using CRU-LSTM Autoencoder

Kim, D., Sim, S., Yoon, B., Liu, L., & Bae, H. (2023) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi58952.2023.00033 · Cited by 2 · see citing works

Dual Contrastive Label Enhancement

Guan, R., Wang, Y., Liu, X., Chen, B., & Zhu, J. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111183 · Cited by 1 · see citing works

Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram

Pratama, Y., Kitano, K., Kushida, T., Fujimura, Y., Funatomi, T., & Mukaigawa, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3554776 · Cited by 1 · see citing works

Towards undetectable adversarial attack on time series classification

Kim, H., Lee, Y., Lee, W., & Lee, J. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122216 · Cited by 1 · see citing works

Classification of time series using information granules for efficient detection of unmanned aerial vehicles faults

Kiersztyn, A., Karczmarek, P., Stęgierski, R., Syta, A., Ambrożkiewicz, B., Jonak, K., Koszewnik, A. P., Ołdziej, D., Dzienis, P., Bazienė, K., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-31720-9 · Cited by 1 · see citing works

Real-time fault diagnosis framework of satellite reaction wheel based on SEMD-UNet and one-dimensional sliding window ResNet

Zhan, M., & Meng, Y. (2026) · Aerospace Science and Technology · doi:10.1016/j.ast.2026.112555 · Cited by 1 · see citing works

LowFreqAttack: An Frequency Attack Method in Time Series Prediction

Xiong, N. N., He, W., & Lu, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28124-2_26 · Cited by 1 · see citing works

Time Series Classification using Improved Deep Learning Approach for Agriculture Field Mapping

Sencaki, D. B., Putri, M. N., Sanjaya, H., Prayogi, H., Anatoly, N., Afifuddin, A., Putra, P. K., Grace, T., & Muhammad Luthfi, A. (2022) · IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS) · doi:10.1109/agers56232.2022.10093560 · Cited by 1 · see citing works

A multivariate LSTM-based deep learning model for stock market prediction

Olotu, S. I. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/2/20220602 · Cited by 1 · see citing works

Review of Time Series Classification Techniques and Methods

Mahmud, W., Fanani, A. Z., Santoso, H. A., & Budiman, F. (2023) · International Seminar on Application for Technology of Information and Communication (iSemantic) · doi:10.1109/isemantic59612.2023.10295319 · Cited by 1 · see citing works

Exploring Hierarchical Forecasting of Data Popularity in High-Energy Physics Experiments

Grigorieva, M., Попова, Н. Н., Vartanov, D. A., & Shubin, M. V. (2023) · Lobachevskii Journal of Mathematics · doi:10.1134/s1995080223080206 · Cited by 1 · see citing works

Method to Predict and Explain Taste Preference using Pork Characteristics and Consumer Information

Jeong, K., Ko, E., Oh, H., Cho, G., Seo, H., Choi, J., Jeong, J.-H., & Lee, E. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444266 · Cited by 1 · see citing works

Early fault detection of current sensors operating in a closed-loop control structure using transfer learning

Skowron, M., Teler, K., & Orłowska-Kowalska, T. (2024) · International Power Electronics and Motion Control Conference (EPE-PEMC) · doi:10.1109/pemc61721.2024.10726361 · Cited by 1 · see citing works

Emotion Loss Attacking: Adversarial Attack Perception for Skeleton Based on Multi-dimensional Features

Liu, F., Xu, Q., & Zheng, Q. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-2684-7_9 · Cited by 1 · see citing works

A Deep Learning Method With Wavelet Packet Transform for Infrared Target Recognition

Yu, B., Lu, H., & Tao, H. (2021) · IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · doi:10.1109/icspcc52875.2021.9564410 · Cited by 1 · see citing works

Deep learning for time series analysis with application on human motion

Ismail-Fawaz, A. (2025) · doi:10.70675/f93c6745ze0fcz42a4z80a0zda361177a181 · Cited by 1 · see citing works

Automatic Ensemble of Deep Learning Using KNN and GA Approaches

Zagagy, B., Herman, M., & Levi, O. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80126-7_43

Comment on tc-2021-194

Madaeni, F., Chokmani, K., Lhissou, R., et al. (2021) · The Cryosphere · doi:10.5194/tc-2021-194-rc2

Comment on tc-2021-194

Madaeni, F., Chokmani, K., Lhissou, R., et al. (2021) · The Cryosphere · doi:10.5194/tc-2021-194-rc1

Adaptive Dila-DenseNet for Image based Time Series Classification in IoT

Jiang, Q., Zhang, S., Chen, J., Chen, X., Huang, H., & Gu, C. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533788

Identification of Microorganism Colony Odor Signature using InceptionTime

Vasconcelos, P. M., Macêdo, D., Almeida, L. M., de Lima-Neto, R. G., Benevides, C. A., Zanchettin, C., & Oliveira, A. L. I. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9658669

Comment on tc-2021-194

Madaeni, F., Chokmani, K., Lhissou, R., et al. (2021) · The Cryosphere · doi:10.5194/tc-2021-194-cc1

Embedded platform based heart murmur classification using deep learning approach

Vakamullu, V., & Mishra, M. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns2.6082

Improving Visual Neuroscience Cell Type Classification with Supervised Machine Learning

Hiatt, J., Howe, D., & Neal, L. (2022) · Systems and Information Engineering Design Symposium · doi:10.1109/sieds55548.2022.9799330

Performance comparison of single and ensemble CNN, LSTM and traditional ANN models for short-term electricity load forecasting

Kathirgamanathan, A., Patel, A., Khwaja, A. S., Venkatesh, B., & Anpalagan, A. (2022) · doi:10.32920/21408579.v1

Classification of Thoracic Pathologies Including COVID-19 in Chest X-ray Images by Using Convolutional Neural Networks

Vajar, P. (2023) · doi:10.32920/24050730.v1

Reply on RC2

Madaeni, F. (2021) · The Cryosphere · doi:10.5194/tc-2021-194-ac3

A Unified Efficient Deep Learning Architecture for Rapid Safety Objects Classification Using Normalized Quantization-Aware Learning

Stephen, O., & Nguyen, M. (2023) · Sensors · doi:10.3390/s23218982

State-of-the-Art Applications of Deep Neural Networks for Time Series Analysis

Bhushan, B., Yuvaraj, S., Magar, H. D., Sahoo, G. S., Haroon, S., & Gupta, M. (2023) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs57948.2023.10434079

An Adversarial Attack Method for Multivariate Time Series Classification Based on AdvGAN

Wang, Y., He, H., Zhang, P., Ma, Y., Lei, Z., & Niu, Z. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2409-6_19

Time Series Forecasting Using the LSTM Model as a Sequential Ensemble Method

Abdoulaye, A., Bamana, A. B., Guiem, R., Atalla, S., & Guidedi, K. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-05832-4_8

Multimodal Fusion Strategies for Multivariate Time Series Classification

Bastos, S. T., Silva, R. D. C., & Silva, D. F. (2025) · doi:10.5753/eniac.2025.14459

Détection des modifications de l’organisation circadienne des activités des animaux en relation avec des états pré-pathologiques, un stress, ou un événement de reproduction

Wagner, N. (2020) · doi:10.70675/170664d0za7f1z4264zb5a9z6755a4164b9b

Ensemble Deep TimeNet: An Ensemble Learning Approach with Deep Neural Networks for Time Series

Pathak, S., Cai, X., & Rajasekaran, S. (2018) · IEEE International Conference on Computational Advances in Bio and Medical Sciences · doi:10.1109/iccabs.2018.8541985 · Cited by 8 · see citing works

Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles

Lines, J., Taylor, S., & Bagnall, A. (2018) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3182382 · Cited by 333 · see citing works

HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification

Lines, J., Taylor, S. L., & Bagnall, A. J. (2016) · Proceedings · doi:10.1109/icdm.2016.0133 · Cited by 177 · 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

Enhancing Time Series Classification with Diversity-Driven Neural Network Ensembles

Abdullayev, J., Devanne, M., Meyer, C., Ismail-Fawaz, A., Weber, J., & Forestier, G. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228372