{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T12:16:10Z","timestamp":1778760970295,"version":"3.51.4"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":49,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and Technology Innovation Commission","doi-asserted-by":"crossref","award":["JCYJ20210324135011030"],"award-info":[{"award-number":["JCYJ20210324135011030"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangdong Pearl River Plan","award":["2019QN01X890"],"award-info":[{"award-number":["2019QN01X890"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-44760-6","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T07:15:25Z","timestamp":1774336525000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A unified time series classification framework via adaptive Gaussian image representation"],"prefix":"10.1038","volume":"16","author":[{"given":"Xinyue","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wai Kin Victor","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"44760_CR1","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1109\/TNSRE.2018.2813138","volume":"26","author":"S Chambon","year":"2018","unstructured":"Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G. & Gramfort, A. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans. Neural Syst. Rehabilit. Eng. 26, 758\u2013769 (2018).","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng."},{"key":"44760_CR2","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1016\/j.procs.2019.12.057","volume":"162","author":"J Xue","year":"2019","unstructured":"Xue, J., Ren, X., Xu, Y. & Feng, Q. Analyzing health seeking behavior of chinese residents and their influencing factors based on chns data. Procedia Comput. Sci. 162, 835\u2013841 (2019).","journal-title":"Procedia Comput. Sci."},{"key":"44760_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112922","volume":"310","author":"J Choi","year":"2025","unstructured":"Choi, J. et al. Early-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional mri time-series. Knowl.-Based Syst. 310, 112922 (2025).","journal-title":"Knowl.-Based Syst."},{"key":"44760_CR4","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.inffus.2021.03.001","volume":"73","author":"N Bahador","year":"2021","unstructured":"Bahador, N., Jokelainen, J., Mustola, S. & Kortelainen, J. Multimodal spatio-temporal-spectral fusion for deep learning applications in physiological time series processing: A case study in monitoring the depth of anesthesia. Inf. Fusion 73, 125\u2013143 (2021).","journal-title":"Inf. Fusion"},{"key":"44760_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123557","volume":"249","author":"F Prinzi","year":"2024","unstructured":"Prinzi, F., Orlando, A., Gaglio, S. & Vitabile, S. Breast cancer classification through multivariate radiomic time series analysis in dce-mri sequences. Expert Syst. Appl. 249, 123557 (2024).","journal-title":"Expert Syst. Appl."},{"key":"44760_CR6","doi-asserted-by":"publisher","first-page":"1612","DOI":"10.3390\/e23121612","volume":"23","author":"Y Xiu","year":"2021","unstructured":"Xiu, Y., Wang, G. & Chan, W. K. V. Crash diagnosis and price rebound prediction in nyse composite index based on visibility graph and time-evolving stock correlation network. Entropy 23, 1612 (2021).","journal-title":"Entropy"},{"key":"44760_CR7","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.eswa.2019.01.022","volume":"123","author":"L Chao","year":"2019","unstructured":"Chao, L., Zhipeng, J. & Yuanjie, Z. A novel reconstructed training-set svm with roulette cooperative coevolution for financial time series classification. Expert Syst. Appl. 123, 283\u2013298 (2019).","journal-title":"Expert Syst. Appl."},{"key":"44760_CR8","doi-asserted-by":"publisher","first-page":"5316","DOI":"10.1109\/TITS.2019.2957325","volume":"21","author":"A Gupta","year":"2020","unstructured":"Gupta, A., Gupta, H. P., Biswas, B. & Dutta, T. An early classification approach for multivariate time series of on-vehicle sensors in transportation. IEEE Trans. Intell. Transp. Syst. 21, 5316\u20135327 (2020).","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"44760_CR9","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s10618-020-00727-3","volume":"35","author":"AP Ruiz","year":"2021","unstructured":"Ruiz, A. P., Flynn, M., Large, J., Middlehurst, M. & Bagnall, A. The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining Knowl. Discov. 35, 401\u2013449 (2021).","journal-title":"Data Mining Knowl. Discov."},{"key":"44760_CR10","doi-asserted-by":"publisher","first-page":"8800","DOI":"10.1007\/s10489-024-05649-x","volume":"54","author":"Y Dong","year":"2024","unstructured":"Dong, Y. et al. Ensemble based fully convolutional transformer network for time series classification. Appl. Intell. 54, 8800\u20138819 (2024).","journal-title":"Appl. Intell."},{"key":"44760_CR11","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"H Ismail Fawaz","year":"2019","unstructured":"Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L. & Muller, P.-A. Deep learning for time series classification: a review. Data Min. Knowl. Discov. 33, 917\u2013963 (2019).","journal-title":"Data Min. Knowl. Discov."},{"key":"44760_CR12","doi-asserted-by":"publisher","first-page":"34515","DOI":"10.1038\/s41598-025-17703-w","volume":"15","author":"HTJ Chan","year":"2025","unstructured":"Chan, H. T. J., \u0160imi\u0107, I. & Veas, E. Explainability and importance estimate of time series classifier via embedded neural network. Sci. Rep. 15, 34515 (2025).","journal-title":"Sci. Rep."},{"key":"44760_CR13","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s10844-012-0196-5","volume":"39","author":"J Lin","year":"2012","unstructured":"Lin, J., Khade, R. & Li, Y. Rotation-invariant similarity in time series using bag-of-patterns representation. J. Intell. Inf. Syst. 39, 287\u2013315 (2012).","journal-title":"J. Intell. Inf. Syst."},{"key":"44760_CR14","unstructured":"Li, D., Lin, J., Bissyand\u00e9, T.\u00a0F., Klein, J. & Traon, Y.\u00a0L. Extracting statistical graph features for accurate and efficient time series classification. Proceedings of the 21st International Conference on Extending Database Technology (EDBT) 12 (2018)."},{"key":"44760_CR15","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1007\/s10618-015-0425-y","volume":"30","author":"MG Baydogan","year":"2016","unstructured":"Baydogan, M. G. & Runger, G. Time series representation and similarity based on local autopatterns. Data Min. Knowl. Discov. 30, 476\u2013509 (2016).","journal-title":"Data Min. Knowl. Discov."},{"key":"44760_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104258","volume":"102","author":"H Wu","year":"2021","unstructured":"Wu, H., Zhao, Y.-P. & Tan, H.-J. A novel neural network based on dynamic time warping and kalman filter for real-time monitoring of supersonic inlet flow patterns. Eng. Appl. Artif. Intell. 102, 104258 (2021).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"44760_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124155","volume":"252","author":"T Lei","year":"2024","unstructured":"Lei, T., Li, J. & Yang, K. Time and frequency-domain feature fusion network for multivariate time series classification. Expert Syst. Appl. 252, 124155 (2024).","journal-title":"Expert Syst. Appl."},{"key":"44760_CR18","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1007\/s10489-025-06695-9","volume":"55","author":"L Xi","year":"2025","unstructured":"Xi, L., Meng, X. & Liu, H. Tffc: time-frequency fusion consistency for semi-supervised time series classification. Appl. Intell. 55, 791 (2025).","journal-title":"Appl. Intell."},{"key":"44760_CR19","doi-asserted-by":"crossref","unstructured":"Ye, L. & Keogh, E. Time series shapelets: A new primitive for data mining. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), KDD \u201909, 947\u2013956 (Association for Computing Machinery, New York, NY, USA, 2009).","DOI":"10.1145\/1557019.1557122"},{"key":"44760_CR20","doi-asserted-by":"crossref","unstructured":"Grabocka, J., Schilling, N., Wistuba, M. & Schmidt-Thieme, L. Learning time-series shapelets. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), KDD \u201914, 392\u2013401 (Association for Computing Machinery, New York, NY, USA, 2014).","DOI":"10.1145\/2623330.2623613"},{"key":"44760_CR21","doi-asserted-by":"publisher","first-page":"41775","DOI":"10.1038\/s41598-025-25667-0","volume":"15","author":"MT Kurbucz","year":"2025","unstructured":"Kurbucz, M. T., Haj\u00f3s, B., Halmos, B. P., Moln\u00e1r, V. \u00c1. & Jakov\u00e1c, A. Adaptive law-based feature representation for time series classification. Sci. Rep. 15, 41775 (2025).","journal-title":"Sci. Rep."},{"key":"44760_CR22","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","volume":"34","author":"H Ismail Fawaz","year":"2020","unstructured":"Ismail Fawaz, H. et al. Finding alexnet for time series classification. Inceptiontime. Data Min. Knowl. Discov. 34, 1936\u20131962 (2020).","journal-title":"Data Min. Knowl. Discov."},{"key":"44760_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"44760_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125755","volume":"266","author":"H Qiu","year":"2025","unstructured":"Qiu, H. et al. A novel two-enhancive aspect module in convolutional neural networks for multivariate time series classification. Expert Syst. Appl. 266, 125755 (2025).","journal-title":"Expert Syst. Appl."},{"key":"44760_CR25","doi-asserted-by":"crossref","unstructured":"Wen, Q. et al. Transformers in time series: A survey. Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI) (2023).","DOI":"10.24963\/ijcai.2023\/759"},{"key":"44760_CR26","first-page":"11106","volume":"35","author":"H Zhou","year":"2021","unstructured":"Zhou, H. et al. Informer: beyond efficient transformer for long sequence time-series forecasting. Proc. AAAI Conf. Artif. Intell. (AAAI) 35, 11106\u201311115 (2021).","journal-title":"Proc. AAAI Conf. Artif. Intell. (AAAI)"},{"key":"44760_CR27","first-page":"11121","volume":"37","author":"A Zeng","year":"2023","unstructured":"Zeng, A., Chen, M., Zhang, L. & Xu, Q. Are transformers effective for time series forecasting?. Proc. AAAI Conf. Artif. Intell. (AAAI) 37, 11121\u201311128 (2023).","journal-title":"Proc. AAAI Conf. Artif. Intell. (AAAI)"},{"key":"44760_CR28","doi-asserted-by":"crossref","unstructured":"Bagnall, A., Davis, L., Hills, J. & Lines, J. Transformation based ensembles for time series classification. Proceedings of the 2012 SIAM International Conference on Data Mining (SDM), 307\u2013318 (2012).","DOI":"10.1137\/1.9781611972825.27"},{"key":"44760_CR29","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.knosys.2018.10.041","volume":"164","author":"R Salles","year":"2019","unstructured":"Salles, R., Belloze, K., Porto, F., Gonzalez, P. H. & Ogasawara, E. Nonstationary time series transformation methods: an experimental review. Knowl.-Based Syst. 164, 274\u2013291 (2019).","journal-title":"Knowl.-Based Syst."},{"key":"44760_CR30","doi-asserted-by":"publisher","first-page":"26297","DOI":"10.1007\/s10489-023-04859-z","volume":"53","author":"X Ma","year":"2023","unstructured":"Ma, X. et al. A time series classification method combining graph embedding and the bag-of-patterns algorithm. Appl. Intell. 53, 26297\u201326312 (2023).","journal-title":"Appl. Intell."},{"key":"44760_CR31","doi-asserted-by":"publisher","first-page":"1850057","DOI":"10.1142\/S0129065718500570","volume":"29","author":"Q Cai","year":"2019","unstructured":"Cai, Q., Gao, Z.-K., Yang, Y.-X., Dang, W.-D. & Grebogi, C. Multiplex limited penetrable horizontal visibility graph from eeg signals for driver fatigue detection. Int. J. Neural Syst. 29, 1850057 (2019).","journal-title":"Int. J. Neural Syst."},{"key":"44760_CR32","doi-asserted-by":"publisher","first-page":"10706","DOI":"10.1007\/s10489-021-02956-5","volume":"52","author":"X Song","year":"2022","unstructured":"Song, X. & Xiao, F. Combining time-series evidence: a complex network model based on a visibility graph and belief entropy. Appl. Intell. 52, 10706\u201310715 (2022).","journal-title":"Appl. Intell."},{"key":"44760_CR33","doi-asserted-by":"publisher","first-page":"3727","DOI":"10.1007\/s11063-022-10783-z","volume":"54","author":"W Jiang","year":"2022","unstructured":"Jiang, W., Zhang, D., Ling, L. & Lin, R. Time series classification based on image transformation using feature fusion strategy. Neural Process. Lett. 54, 3727\u20133748 (2022).","journal-title":"Neural Process. Lett."},{"key":"44760_CR34","doi-asserted-by":"crossref","unstructured":"Homenda, W., Jastrz\u0119bska, A., Pedrycz, W. & Wrzesie\u0144, M. Time series classification with their image representation. Neurocomputing 127214 (2023).","DOI":"10.1016\/j.neucom.2023.127214"},{"key":"44760_CR35","doi-asserted-by":"publisher","first-page":"43800","DOI":"10.1038\/s41598-025-27721-3","volume":"15","author":"H Ling","year":"2025","unstructured":"Ling, H., Hu, B., Shi, D., Huang, Y. & Wang, Y. Radon single-pixel flying target classification via texture-fused lightweight differentiable operators. Sci. Rep. 15, 43800 (2025).","journal-title":"Sci. Rep."},{"key":"44760_CR36","first-page":"3617","volume":"34","author":"Z Cheng","year":"2020","unstructured":"Cheng, Z. et al. Time2graph: revisiting time series modeling with dynamic shapelets. Proc. AAAI Conf. Artif. Intell. (AAAI) 34, 3617\u20133624 (2020).","journal-title":"Proc. AAAI Conf. Artif. Intell. (AAAI)"},{"key":"44760_CR37","doi-asserted-by":"publisher","first-page":"15508","DOI":"10.1038\/srep15508","volume":"5","author":"L Lacasa","year":"2015","unstructured":"Lacasa, L., Nicosia, V. & Latora, V. Network structure of multivariate time series. Sci. Rep. 5, 15508 (2015).","journal-title":"Sci. Rep."},{"key":"44760_CR38","doi-asserted-by":"publisher","first-page":"4972","DOI":"10.1073\/pnas.0709247105","volume":"105","author":"L Lacasa","year":"2008","unstructured":"Lacasa, L., Luque, B., Ballesteros, F., Luque, J. & Nu\u00f1o, J. C. From time series to complex networks: the visibility graph. Proc. Nat. Acad. Sci. 105, 4972\u20134975 (2008).","journal-title":"Proc. Nat. Acad. Sci."},{"key":"44760_CR39","doi-asserted-by":"crossref","unstructured":"Xiu, Y. et al. Time labeled visibility graph for privacy-preserved physiological time series classification. 2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), 280\u2013284 (2022).","DOI":"10.1109\/ICCCBDA55098.2022.9778887"},{"key":"44760_CR40","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.85.046105","volume":"85","author":"JF Donges","year":"2012","unstructured":"Donges, J. F., Heitzig, J., Donner, R. V. & Kurths, J. Analytical framework for recurrence network analysis of time series. Phys. Rev. E 85, 046105 (2012).","journal-title":"Phys. Rev. E"},{"key":"44760_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3063777","volume":"70","author":"N Makaram","year":"2021","unstructured":"Makaram, N., Karthick, P. A. & Swaminathan, R. Analysis of dynamics of emg signal variations in fatiguing contractions of muscles using transition network approach. IEEE Trans. Instrum. Meas. 70, 1\u20138 (2021).","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"44760_CR42","doi-asserted-by":"crossref","unstructured":"Senin, P. & Malinchik, S. Sax-vsm: Interpretable time series classification using sax and vector space model. 2013 IEEE 13th International Conference on Data Mining (ICDM), 1175\u20131180 (2013).","DOI":"10.1109\/ICDM.2013.52"},{"key":"44760_CR43","doi-asserted-by":"publisher","first-page":"162","DOI":"10.21629\/JSEE.2017.01.18","volume":"28","author":"B Zhao","year":"2017","unstructured":"Zhao, B., Lu, H., Chen, S., Liu, J. & Wu, D. Convolutional neural networks for time series classification. J. Syst. Eng. Electron. 28, 162\u2013169 (2017).","journal-title":"J. Syst. Eng. Electron."},{"key":"44760_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1883612.1883613","volume":"43","author":"P Chaovalit","year":"2011","unstructured":"Chaovalit, P., Gangopadhyay, A., Karabatis, G. & Chen, Z. Discrete wavelet transform-based time series analysis and mining. ACM Comput. Surv. 43, 1\u201337 (2011).","journal-title":"ACM Comput. Surv."},{"key":"44760_CR45","doi-asserted-by":"publisher","first-page":"5801","DOI":"10.3390\/s21175801","volume":"21","author":"K Li","year":"2021","unstructured":"Li, K. et al. W-tss: a wavelet-based algorithm for discovering time series shapelets. Sensors 21, 5801 (2021).","journal-title":"Sensors"},{"key":"44760_CR46","first-page":"409","volume":"2018","author":"A Meintjes","year":"2018","unstructured":"Meintjes, A., Lowe, A. & Legget, M. Fundamental heart sound classification using the continuous wavelet transform and convolutional neural networks. Ann. Int. Conf. IEEE Eng. Med. Biol. Soc. 2018, 409\u2013412 (2018).","journal-title":"Ann. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"44760_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112864","volume":"309","author":"VMA Souza","year":"2025","unstructured":"Souza, V. M. A., Veiga, P. S. & Ribeiro, A. G. R. Visemble: a fast ensemble approach for time series classification with multiple visual representations. Knowl.-Based Syst. 309, 112864 (2025).","journal-title":"Knowl.-Based Syst."},{"key":"44760_CR48","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1007\/s40747-021-00296-y","volume":"7","author":"T Li","year":"2021","unstructured":"Li, T., Zhang, Y. & Wang, T. Srpm-cnn: a combined model based on slide relative position matrix and cnn for time series classification. Complex Intell. Syst. 7, 1619\u20131631 (2021).","journal-title":"Complex Intell. Syst."},{"key":"44760_CR49","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/j.neucom.2019.06.032","volume":"359","author":"W Chen","year":"2019","unstructured":"Chen, W. & Shi, K. A deep learning framework for time series classification using relative position matrix and convolutional neural network. Neurocomputing 359, 384\u2013394 (2019).","journal-title":"Neurocomputing"},{"key":"44760_CR50","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1209\/0295-5075\/4\/9\/004","volume":"4","author":"J-P Eckmann","year":"1987","unstructured":"Eckmann, J.-P., Kamphorst, S. O. & Ruelle, D. Recurrence plots of dynamical systems. Europhys. Lett. 4, 973 (1987).","journal-title":"Europhys. Lett."},{"key":"44760_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121978","volume":"238","author":"Y Zhao","year":"2024","unstructured":"Zhao, Y. et al. Image expression of time series data of wearable imu sensor and fusion classification of gymnastics action. Expert Syst. Appl. 238, 121978 (2024).","journal-title":"Expert Syst. Appl."},{"key":"44760_CR52","doi-asserted-by":"publisher","first-page":"12:1","DOI":"10.1145\/2379776.2379788","volume":"45","author":"P Esling","year":"2012","unstructured":"Esling, P. & Agon, C. Time-series data mining. ACM Comput. Surv. 45, 12:1-12:34 (2012).","journal-title":"ACM Comput. Surv."},{"key":"44760_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102006","volume":"101","author":"G Varone","year":"2024","unstructured":"Varone, G. et al. Finger pinching and imagination classification: a fusion of cnn architectures for iomt-enabled bci applications. Inf. Fusion 101, 102006 (2024).","journal-title":"Inf. Fusion"},{"key":"44760_CR54","unstructured":"Dosovitskiy, A. et al. An image is worth 16x16 words: Transformers for image recognition at scale. International Conference on Learning Representations (ICLR) (2020)."},{"key":"44760_CR55","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C. M. Pattern Recognition and Machine Learning (Springer, Berlin, 2006)."},{"key":"44760_CR56","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/S0166-218X(01)00359-6","volume":"122","author":"M Locatelli","year":"2002","unstructured":"Locatelli, M. & Raber, U. Packing equal circles in a square: a deterministic global optimization approach. Discret. Appl. Math. 122, 139\u2013166 (2002).","journal-title":"Discret. Appl. Math."},{"key":"44760_CR57","unstructured":"The best known packings of equal circles in a square (up to n = 10000). https:\/\/www.cs.ucr.edu\/~eamonn\/time_series_data_2018\/ (2022)."},{"key":"44760_CR58","unstructured":"Chen, Y. et al. The ucr time series classification archive. www.cs.ucr.edu\/~eamonn\/time_series_data\/ (2015)."},{"key":"44760_CR59","unstructured":"Bagnall, A. et al. The uea multivariate time series classification archive, 2018 (2018). arXiv:1811.00075."},{"key":"44760_CR60","unstructured":"Wu, H. et al. Timesnet: Temporal 2d-variation modeling for general time series analysis. International Conference on Learning Representations (ICLR) (2023)."},{"key":"44760_CR61","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","volume":"34","author":"A Dempster","year":"2020","unstructured":"Dempster, A., Petitjean, F. & Webb, G. I. Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Discov. 34, 1454\u20131495 (2020).","journal-title":"Data Min. Knowl. Discov."},{"key":"44760_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3182382","volume":"12","author":"J Lines","year":"2018","unstructured":"Lines, J., Taylor, S. & Bagnall, A. Time series classification with hive-cote: the hierarchical vote collective of transformation-based ensembles. ACM Trans. Knowl. Discov. Data 12, 1\u201335 (2018).","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"44760_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2025.107857","volume":"192","author":"VT Pham","year":"2025","unstructured":"Pham, V. T., Zniyed, Y. & Nguyen, T. P. Singular values-driven automated filter pruning. Neural Netw. 192, 107857 (2025).","journal-title":"Neural Netw."},{"key":"44760_CR64","doi-asserted-by":"publisher","first-page":"4358","DOI":"10.1109\/TNNLS.2024.3370294","volume":"36","author":"VT Pham","year":"2025","unstructured":"Pham, V. T., Zniyed, Y. & Nguyen, T. P. Enhanced network compression through tensor decompositions and pruning. IEEE Trans. Neural Netw. Learn. Syst. 36, 4358\u20134370 (2025).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"44760_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106393","volume":"178","author":"VT Pham","year":"2024","unstructured":"Pham, V. T., Zniyed, Y. & Nguyen, T. P. Efficient tensor decomposition-based filter pruning. Neural Netw. 178, 106393 (2024).","journal-title":"Neural Netw."},{"key":"44760_CR66","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1109\/TNNLS.2025.3609797","volume":"37","author":"VT Pham","year":"2026","unstructured":"Pham, V. T., Zniyed, Y. & Nguyen, T. P. Coupled tensor decomposition for compact network representation. IEEE Trans. Neural Netw. Learn. Syst. 37, 617\u2013631 (2026).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44760-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44760-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44760-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T12:03:53Z","timestamp":1778760233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44760-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,24]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["44760"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-44760-6","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,24]]},"assertion":[{"value":"20 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14817"}}