The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Open accessAbstract
In the last 5 years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive. The archive has recently been expanded to 85 data sets, over half of which have been donated by researchers at the University of East Anglia. Aspects of previous evaluations have made comparisons between algorithms difficult. For example, several different programming languages have been used, experiments involved a single train/test split and some used normalised data whilst others did not. The relaunch of the archive provides a timely opportunity to thoroughly evaluate algorithms on a larger number of datasets. We have implemented 18 recently proposed algorithms in a common Java framework and compared them against two standard benchmark classifiers (and each other) by performing 100 resampling experiments on each of the 85 datasets. We use these results to test several hypotheses relating to whether the algorithms are significantly more accurate than the benchmarks and each other. Our results indicate that only nine of these algorithms are significantly more accurate than both benchmarks and that one classifier, the collective of transformation ensembles, is significantly more accurate than all of the others. All of our experiments and results are reproducible: we release all of our code, results and experimental details and we hope these experiments form the basis for more robust testing of new algorithms in the future.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“HIVE-COTE is currently considered the state-of-the-art algorithm for time series classification (Bagnall et al., 2017) when evaluated over the 85 datasets from the UCR/UEA archive.”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
In the citing paper
“To achieve its high accuracy, HIVE-COTE becomes hugely computationally intensive and impractical to run on a real big data mining problem (Bagnall et al., 2017).”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
In the citing paper
“Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task.”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
In the citing paper
“Similarly to Bagnall et al. (2017), in order to have a fair comparison between the tested approaches, we developed a common framework in Python, Keras (Chollet, 2015) and Tensorflow (Abadi et al., 2015) to train the deep learning models on a cluster of more than 60 GPUs.”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
In the citing paper
“Note that the time series in this archive are already z-normalized (Bagnall et al., 2017).”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
In the citing paper
“This archive also exhibits datasets with different characteristics such as the length of the time series which, unlike the UCR/UEA archive, varies among the same dataset. This is due to the fact that the datasets in the UCR/UEA archive are already re-scaled to have an equal length among one dataset (Bagnall et al., 2017).”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
In the citing paper
“In order to have a thorough and fair experimental evaluation of all approaches, we tested each algorithm on the whole UCR/UEA archive (Chen et al., 2015b; Bagnall et al., 2017) which contains 85 univariate time series datasets.”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
In the citing paper
“The informal consensus in the literature is that generative models are usually less accurate than direct discriminative models (Bagnall et al., 2017; Nguyen et al., 2017);”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
In the citing paper
“To measure the performance of the model on the test data (generalization), we adopted the accuracy measure (similarly to Bagnall et al. (2017)).”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
In the citing paper
“However, given the small sample size (only 7 datasets), we cannot conclude that FCN will almost always outperform the ResNet model for ECG datasets (Bagnall et al., 2017).”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
In the citing paper
“This straightforward approach supposes that the ED is able to strongly separate the raw time series, which is usually not the case evident by the low accuracy of the nearest neighbor when coupled with ED (Bagnall et al., 2017).”
From Deep learning for time series classification: a review (2019) · cites this paper as Bagnall et al., 2017
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 12,051
Domain-Adversarial Training of Neural Networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318
Experiencing SAX: A novel symbolic representation of time series
Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-007-0064-z · Cited by 1,618
Self-citationQuerying and mining of time series data
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., & Keogh, E. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1454159.1454226 · Cited by 1,353
Self-citationGarcía, S., & Herrera, F. (2008) · Cited by 1,277
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Keogh, E., & Kasetty, S. (2003) · Data Mining and Knowledge Discovery · doi:10.1023/a:1024988512476 · Cited by 860
Self-citationExperimental comparison of representation methods and distance measures for time series data
Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., & Keogh, E. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0250-5 · Cited by 846
Self-citationOn The Marriage of Lp-norms and Edit Distance
Chen, L., & Ng, R. T. (2004) · Elsevier eBooks · doi:10.1016/b978-012088469-8.50070-x · Cited by 792
Algorithms for the Longest Common Subsequence Problem
Hirschberg, D. S. (1977) · Journal of the ACM · doi:10.1145/322033.322044 · Cited by 752
Cited by
Sorted by citations
Items 801–900 of 1,000
A unified framework for financial commentary prediction
Ozyegen, O., Malik, G., Çevik, M., Ioi, K., & Mokhtari, K. E. (2024) · Information Technology and Management · doi:10.1007/s10799-024-00439-w · Cited by 4
CTCTime: A New Model for Unidimensional Time Series Classification
Lan, G., Tang, J., & Guo, F. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11694-x · Cited by 4
Feature-fused residual network for time series classification
Wei, Y., Du, M., Li, T., Zheng, X., & Ji, C. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.102227 · Cited by 5
Language task-based fMRI analysis using machine learning and deep learning
Kuan, E., Vegh, V., Phamnguyen, J., O’Brien, K., Hammond, A., & Reutens, D. C. (2024) · Frontiers in Radiology · doi:10.3389/fradi.2024.1495181 · Cited by 4
FedDict: Towards Practical Federated Dictionary-Based Time Series Classification
Liang, Z., Liang, Z., Wang, H., & Zheng, B. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3528023 · Cited by 4
Learning Location-Guided Time-Series Shapelets
Yamaguchi, A., Ueno, K., & Kashima, H. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3536462 · Cited by 4
Deep multimodal learning for time series analysis in social computing: a survey
Yang, C., Chen, Y., Li, Z., Wang, X., Shi, K., Yao, L., Xu, G., & Guo, Z. (2025) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-025-00363-x · Cited by 5
Oh, Y., Kim, H., & Kim, S. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111393 · Cited by 5
Arai, K., Willmes, M., Johnson, R. C., & Sturrock, A. M. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.103604 · Cited by 4
Improved FTIR-based classification for food authentication using a topological ensemble framework
Yu, H.-C., & Chen, Y. (2026) · Current Research in Food Science · doi:10.1016/j.crfs.2026.101441 · Cited by 4
Discovering the Arrow of Time in Machine Learning
Kasmire, J., & Zhao, A. (2021) · Information · doi:10.3390/info12110439 · Cited by 4
Sun, H., Zhu, L., Guo, L., Luo, Y., Du, D., & Sun, Y. (2021) · Natural Hazards · doi:10.1007/s11069-021-05041-9 · Cited by 4
Time series analysis using persistent homology of distance matrix
Ichinomiya, T. (2023) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.14.79 · Cited by 4
A time series classification method combining graph embedding and the bag-of-patterns algorithm
Ma, X., Yu, M., Huang, H., Hou, R., Dong, M., Ota, K., & Zeng, D. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04859-z · Cited by 4
Time-series Shapelets with Learnable Lengths
Yamaguchi, A., Ueno, K., & Kashima, H. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615082 · Cited by 4
A Hands-on Introduction to Time Series Classification and Regression
Bagnall, A. J., Middlehurst, M., Forestier, G., Ismail-Fawaz, A., Guillaume, A., Guijo-Rubio, D., Tan, C. W., Dempster, A., & Webb, G. I. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671443 · Cited by 4
Self-citationNon-elastic time series fuzzy clustering for efficient analysis of industrial data sets
Stržinar, Ž., Pregelj, B., & Škrjanc, I. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112398 · Cited by 4
Anomaly Detection in Spacecraft Telemetry: Forecasting vs. Classification
Lakey, D. T., & Schlippe, T. (2024) · IEEE Space Computing Conference (SCC) · doi:10.1109/scc61854.2024.00009 · Cited by 4
Byun, Y., Son, J., Yun, J., Choo, H., & Won, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-91497-9 · Cited by 4
Ng, G., & Andrysek, J. (2025) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01638-4 · Cited by 5
Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms?
Large, J., Southam, P., & Bagnall, A. J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29859-3_5 · Cited by 4
Self-citationA Knowledge-Based Computational Environment for Real-World Data Processing
Tianxing, M., Zhukova, N. A., Meltsov, V., & Shichkina, Y. A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-24289-3_20 · Cited by 4
Fahle, S., Kneissler, A., Gläser, T., & Kuhlenkötter, B. (2020) · Lecture notes in production engineering · doi:10.1007/978-3-662-62138-7_49 · Cited by 4
Development of a Multi-sensor Emotional Response System for Social Robots
Hefter, E., Perry, C., Coiro, N., Parsons, H. M., Zhu, S., & Li, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87725-5_8 · Cited by 4
Video Stream Recognition Using Bitstream Shape for Mobile Network QoE
Chmieliauskas, D., & Paulikas, Š. (2023) · Sensors · doi:10.3390/s23052548 · Cited by 4
Ruble, M., Hayes, C. E., Welborn, M., Zajic, A. G., Prvulović, M., & Pitruzzello, A. M. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2019.2919002 · Cited by 4
fierClass: A multi-signal, cepstrum-based, time series classifier
Gelmini, S., Formentin, S., Strada, S. C., Tanelli, M., & Savaresi, S. M. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103262 · Cited by 4
Sufficient conditions for the existence of a sample mean of time series under dynamic time warping
Jain, B. J., & Schultz, D. (2020) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-019-09682-2 · Cited by 4
Pal, A., Ukil, A., Deb, T., Sahu, I., & Majumdar, A. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054418 · Cited by 4
Lixin, L., & Lin, L. (2020) · International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS) · doi:10.1109/hpbdis49115.2020.9130588 · Cited by 4
Deep Learning for In-Vehicle Intrusion Detection System
Gherbi, E., Hanczar, B., Janodet, J.-C., & Klaudel, W. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-63820-7_6 · Cited by 4
Unobserved classes and extra variables in high-dimensional discriminant analysis
Fop, M., Mattei, P., Bouveyron, C., & Murphy, T. B. (2022) · Advances in Data Analysis and Classification · doi:10.1007/s11634-021-00474-3 · Cited by 4
Uncertain Time Series Classification with Shapelet Transform
Mbouopda, M. F., & Nguifo, E. M. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00044 · Cited by 4
Novel Features for Binary Time Series Based on Branch Length Similarity Entropy
Lee, S., & Park, C.-M. (2021) · Entropy · doi:10.3390/e23040480 · Cited by 4
A New Measure to Characterize the Self-Similarity of Binary Time Series and its Application
Lee, S., & Park, C.-M. (2021) · IEEE Access · doi:10.1109/access.2021.3081400 · Cited by 4
Randomized trees for time series representation and similarity
Görgülü, B., & Baydoğan, M. G. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108097 · Cited by 4
Klein, P., Weingarz, N., & Bergmann, R. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533544 · Cited by 4
$$\kappa $$-Circulant Maximum Variance Bases
Bonenberger, C. M. A., Ertel, W., & Schneider, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87626-5_2 · Cited by 4
Xu, D., Hu, R., Xiong, Z., Wang, Z., Luo, L., & Li, D. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475664 · Cited by 4
Agarwal, S., Nguyen, T. T. T., Nguyen, T. L., & Ifrim, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91445-5_1 · Cited by 4
Parameter continuity in time-varying Gauss–Markov models for learning from small training data sets
Ron, M., Burget, P., & Hlaváč, V. (2022) · Information Sciences · doi:10.1016/j.ins.2022.02.037 · Cited by 4
Bhattacharya, S., Ambrose, W. A., Ko, L. ‘., & Casey, B. (2022) · Interpretation · doi:10.1190/int-2021-0165.1 · Cited by 4
Strunz, E., Zyabkina, O., & Meyer, J. (2022) · International Conference on Harmonics and Quality of Power · doi:10.1109/ichqp53011.2022.9808558 · Cited by 4
Feature Subset Selection for Detecting Fatigue in Runners Using Time Series Sensor Data
Kathirgamanathan, B., Buckley, C., Caulfield, B., & Cunningham, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09037-0_44 · Cited by 4
Prediction of declining engagement to self-monitoring apps on the example of tinnitus mHealth data
Schleicher, M., Hamacher, S., Naujoks, M., Günther, K., Schmidt, T., Pryss, R., Schobel, J., Schlee, W., & Spiliopoulou, M. (2022) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms55023.2022.00047 · Cited by 4
Bidenko, S. I., Khramov, I. S., Bengert, A. A., & Muchkaeva, I. S. (2022) · HYDROMETEOROLOGY AND ECOLOGY PROCEEDINGS OF THE RUSSIAN STATE HYDROMETEOROLOGICAL UNIVERSITY · doi:10.33933/2713-3001-2022-68-508-524 · Cited by 4
Reducing the Computational Complexity of Learning with Random Convolutional Features
Omidi, M. A., Seyfe, B., & Valaee, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095893 · Cited by 4
Dual Deep Learning model for Electricity Price Forecasting: Bi-LSTM and GRU fusion
Raju, R., Anjali, T., & Ramlal, N. P. (2023) · International Conference on Contemporary Computing · doi:10.1145/3607947.3607951 · Cited by 4
Research on Table Tennis Swing Recognition Based on Lightweight OpenPose
Chen, P., & Shen, Q. (2023) · International Conference on Advanced Computer Theory and Engineering · doi:10.1109/icacte59887.2023.10335442 · Cited by 4
Rotti, S. A., Aydin, B., & Martens, P. C. H. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad374e · Cited by 4
Studying the Effect of Different $$L_p$$ Norms in the Context of Time Series Ordinal Classification
Guijo-Rubio, D., Vargas, V. M., Gutiérrez, P. A., & Hervás‐Martínez, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85713-4_5 · Cited by 4
Deep learning for supervised classification of temporal data in ecologyPreprint
Capinha, C., Ceia‐Hasse, A., Kramer, A. M., & Meijer, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.14.296251 · Cited by 4
Reservoir-based Tracking (TRAKR) For One-shot Classification Of Neural Time-series PatternsPreprint
Afzal, M. F., Márton, C. D., Rich, E. L., Mayberg, H. S., & Rajan, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.13.464288 · Cited by 4
Stržinar, Ž., Pregelj, B., Petrovc̆ic̆, J., Škrjanc, I., & Dolanč, G. (2024) · Data in Brief · doi:10.1016/j.dib.2024.110619 · Cited by 4
Zhang, H., & Feng, J. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394159 · Cited by 3
Vanacore, A., & Ciardiello, A. (2025) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2025.102240 · Cited by 3
Ma, C., Li, J., Qi, X., Sun, M., & Zhou, H. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112294 · Cited by 5
Lara-Abelenda, F. J., Chushig-Muzo, D., Acosta, C. B., Wägner, A. M., Granja, C., & Soguero-Ruíz, C. (2025) · IEEE Access · doi:10.1109/access.2025.3600917 · Cited by 4
A graph convolutional network for time series classification using recurrence plots
Kang, H., Lee, T.-H., & Lee, J. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06841-3 · Cited by 3
PatternFusion: a hybrid model for pattern recognition in time-series data using ensemble learning
Bouchelligua, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-28649-4 · Cited by 4
Bilen, A., Decman, M., Stamer, F., & Lanza, G. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae739c · Cited by 3
Ospina-Dávila, Y. M., & Orozco‐Alzate, M. (2022) · Computers & Structures · doi:10.1016/j.compstruc.2022.106754 · Cited by 3
Ciortuz, G., Grzegorzek, M., & Fudickar, S. (2023) · International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) · doi:10.1145/3615834.3615858 · Cited by 3
Proud, J. K., Garofolini, A., Mudie, K. L., Lai, D. T. H., & Begg, R. K. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2024.1282867 · Cited by 3
An efficient CNN-transformer hybrid approach for water turbine unit failure prediction
Zhang, K., He, M. X., & Guo, J. (2024) · Journal of Computational Methods in Sciences and Engineering · doi:10.3233/jcm-237074 · Cited by 3
Chen, W., & Li, J. (2024) · IEEE Access · doi:10.1109/access.2024.3413948 · Cited by 3
Schlegel, K., Rachkovskij, D. A., Osipov, E., Protzel, P., & Neubert, P. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650604 · Cited by 3
A Novel Method for Code Clone Detection Based on Minimally Random Kernel Convolutional Transform
Abdelkader, M. (2024) · IEEE Access · doi:10.1109/access.2024.3484995 · Cited by 3
Generalized Time Series Classification via Component Decomposition and Alignment
Cheng, Y., Lee, D., Oberhauser, H., & Li, H. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3527215 · Cited by 3
Dilated Convolution for Time Series Learning
Zhang, W., Das, S., Nguyen, L. M., & Daniel, L. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887837 · Cited by 3
Hosseinzadeh, P., Boubrahimi, S. F., & Hamdi, S. M. (2025) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/adb1c4 · Cited by 4
A novel deep learning approach to online quality control for multiple time series process
Tian, G., Liu, Y., Xue, L., Wang, N., Liang, X., & Sun, J. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111102 · Cited by 3
Yi, Y., Zhao, P., Sheng, H., Liu, M., Li, S., Dai, J., Kong, J., & Qiao, S. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113474 · Cited by 3
A Full-Optical Pretouch Dual-Modal and Dual-Mechanism (PDM2) Sensor for Robotic Grasping
Fang, C., Yan, Z., Guo, F., Li, S., Song, D., & Zou, J. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127586 · Cited by 3
Aging and activity patterns: actigraphy evidence from NHANES studies
Luo, W., Scharf, M. T., & Androulakis, I. P. (2025) · Frontiers in Systems Biology · doi:10.3389/fsysb.2025.1632110 · Cited by 3
Neuro-Ensemble for Time Series Data Classification
Boubrahimi, S. F., Ma, R., & Angryk, R. A. (2018) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00015 · Cited by 3
Domain-Oriented Multilevel Ontology for Adaptive Data Processing
Tianxing, M., Stankova, E. N., Vodyaho, A. I., Zhukova, N. A., & Shichkina, Y. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58799-4_46 · Cited by 3
Evaluating the Lottery Ticket Hypothesis to Sparsify Neural Networks for Time Series Classification
Schlake, G. S., Hüwel, J. D., Berns, F., & Beecks, C. (2022) · IEEE International Conference on Data Engineering Workshops (ICDEW) · doi:10.1109/icdew55742.2022.00015 · Cited by 3
Early prediction of Ibex 35 movements
García, M. M., Vargas, M. J. S., Mori, U., & Lozano, J. A. (2022) · Journal of Forecasting · doi:10.1002/for.2933 · Cited by 3
Adaptive Shapelet Selection for Time Series Classification
Wei, Y., Wang, Y., Du, M., Hu, Y., & Ji, C. (2023) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd57460.2023.10152664 · Cited by 3
Time Series Classification of Electroencephalography Data
Rushbrooke, A., Tsigarides, J., Sami, S. A. K., & Bagnall, A. J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43085-5_48 · Cited by 3
Self-citationKeshavarzian, A., & Valaee, S. (2023) · Machine learning for signal processing ... · doi:10.1109/mlsp55844.2023.10285973 · Cited by 3
Braig, M., & Zeiler, P. (2024) · IEEE Access · doi:10.1109/access.2024.3384697 · Cited by 3
Safety control strategy of spinal lamina cutting based on force and cutting depth signals
Zhang, J., Zhang, Y., Liu, S., Ji, X., Liu, S., Li, Z., Geng, B., Li, W., & Wang, T. (2024) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12341 · Cited by 3
DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data
Inan, M. S. K., Liao, K., Shen, H., Jayaraman, P. P., Georgakopoulos, D., & Tang, M. J. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-63989-0_6 · Cited by 3
Wang, H., Peng, B., Zhang, J., & Cheng, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650281 · Cited by 3
ChatKG: Visualizing time-series patterns aided by intelligent agents and a knowledge graph
Christino, L., & Paulovich, F. V. (2024) · Computers & Graphics · doi:10.1016/j.cag.2024.104092 · Cited by 3
Solana, A., Fransén, E. A., & Uribarri, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-77066-1_6 · Cited by 3
Predicting hydro turbine failures through digital twin simulations of rare real-world data
Guo, J., Zhang, Y., & Wang, W. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978241304260 · Cited by 3
Additive Attention for Vetting Transiting Exoplanet Candidates
Carnerero, À. H., Sànchez–Marrè, M., & Morales, J. C. (2025) · The Astronomical Journal · doi:10.3847/1538-3881/add2f1 · Cited by 4
Time Series Motif Discovery: A Comprehensive Evaluation
Guerrmi, V., Germain, T., Truong, C., Oudre, L., & Boniol, P. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3734839.3734857 · Cited by 3
Evolving clustering of time series for unsupervised analysis of industrial data streams
Stržinar, Ž., Škrjanc, I., Pratama, M., & Pregelj, B. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111508 · Cited by 3
Sunilkumar, S. B., Arvind, R. Y., Mihir, M. P., & Mitul, M. P. (2017) · International Conference On Smart Technologies For Smart Nation (SmartTechCon) · doi:10.1109/smarttechcon.2017.8358393 · Cited by 3
ALoT: A Time-Series Similarity Measure Based on Alignment of Textures
Oğul, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03493-1_60 · Cited by 3
MACD-Histogram-based Fully Convolutional Neural Networks for Classifying Time Series
Hashida, S., & Tamura, K. (2019) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit.2019.8820629 · Cited by 3
A Proposal for Shape Aware Feature Extraction for Time Series Classification
Ito, H., & Chakraborty, B. (2019) · International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icawst.2019.8923166 · Cited by 3
Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data
Lemus, M., Beirão, J. P., Paunković, N., Carvalho, A. M., & Mateus, P. (2019) · Entropy · doi:10.3390/e22010049 · Cited by 3
Multivariate Time Series Classification: A Relational Way
Gay, D., Bondu, A., Lemaire, V., Boullé, M., & Clérot, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59065-9_25 · Cited by 3
An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals
Manoni, L., Turchetti, C., & Falaschetti, L. (2020) · IEEE Access · doi:10.1109/access.2020.3038314 · Cited by 3
Large Astronomical Time Series Pre-processing for Classification Using Artificial Neural Networks
Andrešič, D., Šaloun, P., & Pečíková, B. (2021) · Emergence, complexity and computation · doi:10.1007/978-3-030-65867-0_12 · Cited by 3
Metadata
- Authors
- Bagnall, A., Lines, J., Bostrom, A., Large, J., & Keogh, E.
- Affiliations
- University of East Anglia (GB)
- University of California, Riverside (US)
- Corresponding author
- Bagnall, Anthony
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2016-11-23
- Volume
- 31
- Issue
- 3
- Pages
- 606-660
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- PMC
- PMC6404674
- OpenAlex
- W2555077524
- Cited by
- 1,377
- References
- 48
- Citations by year
- 2026: 90 · 2025: 133 · 2024: 166 · 2023: 185 · 2022: 187 · 2021: 195 · 2020: 199 · 2019: 130 · 2018: 69 · 2017: 21 · 2016: 1
- FWCI
- 81.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Data Mining and Knowledge Discovery (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/M015087/1)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- time series classification, benchmark dataset, algorithm comparison, classification accuracy, reproducibility, Java implementation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
