{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:59:13Z","timestamp":1762955953424,"version":"3.45.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:00:00Z","timestamp":1760745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T00:00:00Z","timestamp":1760745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Fundamental Research Funds for the Universities of Henan Province","award":["No. NSFRF220415, NSFRF230503"],"award-info":[{"award-number":["No. NSFRF220415, NSFRF230503"]}]},{"name":"Henan University of Technology Outstanding Youth Science Fund","award":["J2025-3"],"award-info":[{"award-number":["J2025-3"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 52404163"],"award-info":[{"award-number":["No. 52404163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Henan Polytechnic University","award":["No. B2021-31"],"award-info":[{"award-number":["No. B2021-31"]}]},{"DOI":"10.13039\/501100006407","name":"Henan Natural Science Foundation","doi-asserted-by":"crossref","award":["No. 222300420168"],"award-info":[{"award-number":["No. 222300420168"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Young Backbone Teachers Training Program of Henan Polytechnic University","award":["No. 2025XQG-13"],"award-info":[{"award-number":["No. 2025XQG-13"]}]},{"name":"Program for Science\uff06Technology Innovation Talents in Universities of Henan Province","award":["NO. 26HASTIT017"],"award-info":[{"award-number":["NO. 26HASTIT017"]}]},{"name":"Science and Technology Plan Project of Henan Province","award":["No. 232102211007 and 252102221003"],"award-info":[{"award-number":["No. 232102211007 and 252102221003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s00607-025-01573-1","type":"journal-article","created":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T08:15:19Z","timestamp":1760775319000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["R-VABED: a nonlinear time series prediction model based on rolling decomposition and bidirectional LSTM"],"prefix":"10.1007","volume":"107","author":[{"given":"Hongxing","family":"Peng","sequence":"first","affiliation":[]},{"given":"Shuxia","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Jianji","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Haiqing","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yongliang","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Xin","sequence":"additional","affiliation":[]},{"given":"Yanan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yunfeng","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,18]]},"reference":[{"issue":"6","key":"1573_CR1","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1007\/s42979-024-03060-4","volume":"5","author":"S Saleti","year":"2024","unstructured":"Saleti S, Panchumarthi LY, Kallam YR, Parchuri L, Jitte S (2024) Enhancing forecasting accuracy with a moving Average-Integrated hybrid ARIMA-LSTM Model[J]. SN Comput Sci 5(6):704. https:\/\/doi.org\/10.1007\/s42979-024-03060-4","journal-title":"SN Comput Sci"},{"issue":"22","key":"1573_CR2","doi-asserted-by":"publisher","first-page":"5298","DOI":"10.5897\/IJPS11.1180","volume":"6","author":"A Najah","year":"2011","unstructured":"Najah A, El-Shafie A, Karim OA et al (2011) An application of different artificial intelligences techniqu-es for water quality prediction[J]. Int J Phys Sci 6(22):5298\u20135308. https:\/\/doi.org\/10.5897\/IJPS11.1180","journal-title":"Int J Phys Sci"},{"issue":"1","key":"1573_CR3","doi-asserted-by":"publisher","first-page":"103550","DOI":"10.1016\/j.jksus.2024.103550","volume":"36","author":"Y Yuan","year":"2024","unstructured":"Yuan Y, Yang Q, Ren J et al (2024) Short-term wind power prediction based on IBOA-AdaBoost-RVM[J]. J King Saud Univ \u2013 Sci 36(1):103550. https:\/\/doi.org\/10.1016\/j.jksus.2024.103550","journal-title":"J King Saud Univ \u2013 Sci"},{"key":"1573_CR4","doi-asserted-by":"publisher","first-page":"30223","DOI":"10.1109\/ACCESS.2020.2972435","volume":"8","author":"X Zhang","year":"2020","unstructured":"Zhang X, Mohanty SN, Parida AK et al (2020) Annual and Non-Monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha[J]. IEEE Access 8:30223\u201330233. https:\/\/doi.org\/10.1109\/ACCESS.2020.2972435","journal-title":"IEEE Access"},{"key":"1573_CR5","doi-asserted-by":"publisher","first-page":"3267","DOI":"10.1007\/s00500-023-09606-7","volume":"28","author":"ET Sivadasan","year":"2024","unstructured":"Sivadasan ET, Mohana Sundaram N, Santhosh R (2024) Stock market forecasting using deep learning with long Short-Term memory and gated recurrent Unit[J]. Soft Comput 28:3267\u20133282. https:\/\/doi.org\/10.1007\/s00500-023-09606-7","journal-title":"Soft Comput"},{"issue":"8","key":"1573_CR6","doi-asserted-by":"publisher","first-page":"598","DOI":"10.3390\/fractalfract7080598","volume":"7","author":"YB \u00d6z\u00e7elik","year":"2023","unstructured":"\u00d6z\u00e7elik YB, Altan A (2023) Overcoming nonlinear dynamics in diabetic retinopathy classification: A robust AI-Based model with chaotic swarm intelligence optimization and recurrent long Short-Term Memory[J]. Fractal Fract 7(8):598. https:\/\/doi.org\/10.3390\/fractalfract7080598","journal-title":"Fractal Fract"},{"key":"1573_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2021.106570","author":"AA Torky","year":"2021","unstructured":"Torky AA, Ohno S (2021) Comput Struct. https:\/\/doi.org\/10.1016\/j.compstruc.2021.106570. Deep Learning Techniques for Predicting Nonlinear Multi-Component Seismic Responses of Structural Buildings[J],252:106570"},{"key":"1573_CR8","doi-asserted-by":"publisher","unstructured":"Qin Y, Song D, Chen H et al (2017) A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction[J]. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). https:\/\/doi.org\/10.48550\/arXiv.1704.02971","DOI":"10.48550\/arXiv.1704.02971"},{"key":"1573_CR9","doi-asserted-by":"publisher","first-page":"e2393","DOI":"10.7717\/peerj-cs.2393","volume":"10","author":"C Bar\u0131\u015f","year":"2024","unstructured":"Bar\u0131\u015f C, Yanarate\u015f C, Altan A (2024) A robust Chaos-Inspired artificial intelligence model for dealing with nonlinear dynamics in wind speed Forecasting[J]. PeerJ Comput Sci 10:e2393. https:\/\/doi.org\/10.7717\/peerj-cs.2393","journal-title":"PeerJ Comput Sci"},{"key":"1573_CR10","doi-asserted-by":"publisher","unstructured":"Gao R, Du L, Suganthan PN et al Random vector functional link neural network based ensemble deep learning for short-term load forecasting[J]. Expert Syst Appl, 2022,206:117784.https:\/\/doi.org\/10.1016\/j.eswa.2022.117784","DOI":"10.1016\/j.eswa.2022.117784"},{"key":"1573_CR11","doi-asserted-by":"publisher","first-page":"3947","DOI":"10.1007\/s40435-024-01468-7","volume":"12","author":"I Bouaissi","year":"2024","unstructured":"Bouaissi I, Rezig A, Laib A et al (2024) Real-Time detection of bearing faults through a hybrid WTMP analysis of Frequency-Related States[J]. Int J Dynamics Control 12:3947\u20133962. https:\/\/doi.org\/10.1007\/s40435-024-01468-7","journal-title":"Int J Dynamics Control"},{"issue":"2","key":"1573_CR12","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1007\/s10462-022-10195-4","volume":"56","author":"F J\u00e1\u00f1ez-Martino","year":"2023","unstructured":"J\u00e1\u00f1ez-Martino F, Alaiz-Rodr\u00edguez R, Gonz\u00e1lez-Castro V et al (2023) A review of spam email detection: a-nalysis of spammer strategies and the dataset shift problem[J]. Artif Intell Rev 56(2):1145\u20131173. https:\/\/doi.org\/10.1007\/s10462-022-10195-4","journal-title":"Artif Intell Rev"},{"key":"1573_CR13","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1098\/rsta.1927.0007","volume":"226","author":"GU Yule","year":"1927","unstructured":"Yule GU (1927) On a method of investigating periodicities disturbed series, with special reference to wolfer\u2019s sunspot numbers | philosophical transactions of the Royal society of London. Series A, containing papers of a mathematical or physical Character[J]. Philosophical Trans Royal 226:267\u2013298. https:\/\/doi.org\/10.1098\/rsta.1927.0007","journal-title":"Philosophical Trans Royal"},{"key":"1573_CR14","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.jhydrol.2017.02.012","volume":"547","author":"H Moeeni","year":"2017","unstructured":"Moeeni H, Bonakdari H, Fatemi SE (2017) Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction[J]. J Hydrol 547:348\u2013364. https:\/\/doi.org\/10.1016\/j.jhydrol.2017.02.012","journal-title":"J Hydrol"},{"issue":"6","key":"1573_CR15","doi-asserted-by":"publisher","first-page":"5309","DOI":"10.1109\/TVT.2019.2912893","volume":"68","author":"J Guo","year":"2019","unstructured":"Guo J, He H, Sun C (2019) ARIMA-Based road gradient and vehicle velocity prediction for hybrid Ele-ctric vehicle energy Management[J]. IEEE Trans Veh Technol 68(6):5309\u20135320. https:\/\/doi.org\/10.1109\/TVT.2019.2912893","journal-title":"IEEE Trans Veh Technol"},{"issue":"4","key":"1573_CR16","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1109\/TITS.2017.2711046","volume":"19","author":"C Ding","year":"2018","unstructured":"Ding C, Duan J, Zhang Y et al (2018) Using an ARIMA-GARCH modeling approach to improve subway Short-Term ridership forecasting accounting for dynamic Volatility[J]. IEEE Trans Intell Transp Syst 19(4):1054\u20131064. https:\/\/doi.org\/10.1109\/TITS.2017.2711046","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"12","key":"1573_CR17","doi-asserted-by":"publisher","first-page":"7743","DOI":"10.1109\/TII.2020.2970165","volume":"16","author":"AT Eseye","year":"2020","unstructured":"Eseye AT, Lehtonen M (2020) Short-Term forecasting of heat demand of buildings for efficient and optimal energy management based on integrated machine learning Models[J]. IEEE Trans Industr Inf 16(12):7743\u20137755. https:\/\/doi.org\/10.1109\/TII.2020.2970165","journal-title":"IEEE Trans Industr Inf"},{"issue":"2","key":"1573_CR18","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1109\/TPWRS.2016.2569608","volume":"32","author":"KY Bae","year":"2017","unstructured":"Bae KY, Jang HS, Sung DK (2017) Hourly solar irradiance prediction based on support vector Mach-ine and its error Analysis[J]. IEEE Trans Power Syst 32(2):935\u2013945. https:\/\/doi.org\/10.1109\/TPWRS.2016.2569608","journal-title":"IEEE Trans Power Syst"},{"key":"1573_CR19","doi-asserted-by":"publisher","first-page":"5769","DOI":"10.1007\/s00202-024-02821-x","volume":"107","author":"Y Yuan","year":"2025","unstructured":"Yuan Y, Yang Q, Ren J, Zhao X, Li M (2025) Short-term power load forecasting based on SKDR hybrid model[J]. Electr Eng 107:5769\u20135785. https:\/\/doi.org\/10.1007\/s00202-024-02821-x","journal-title":"Electr Eng"},{"issue":"5","key":"1573_CR20","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1109\/TII.2015.2431642","volume":"11","author":"S Buhan","year":"2015","unstructured":"Buhan S, \u00c7ad\u0131rc\u0131 I (2015) Multistage Wind-Electric power forecast by using a combination of advanced statistical Methods[J]. IEEE Trans Industr Inf 11(5):1231\u20131242. https:\/\/doi.org\/10.1109\/TII.2015.2431642","journal-title":"IEEE Trans Industr Inf"},{"issue":"4","key":"1573_CR21","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1109\/TNNLS.2020.2985720","volume":"32","author":"K Bandara","year":"2021","unstructured":"Bandara K, Bergmeir C, Hewamalage H (2021) LSTM-MSNet: leveraging forecasts on sets of related time series with multiple seasonal Patterns[J]. IEEE Trans Neural Networks Learn Syst 32(4):1586\u20131599. https:\/\/doi.org\/10.1109\/TNNLS.2020.2985720","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"2","key":"1573_CR22","doi-asserted-by":"publisher","first-page":"115426","DOI":"10.1016\/j.oceaneng.2023.115426","volume":"285","author":"Y Yuan","year":"2023","unstructured":"Yuan Y, Shen Q, Xi W et al (2023) Multidisciplinary design optimization of dynamic positioning system for semi-submersible platform[J]. Ocean Eng 285(2):115426. https:\/\/doi.org\/10.1016\/j.oceaneng.2023.115426","journal-title":"Ocean Eng"},{"issue":"2","key":"1573_CR23","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/TETCI.2019.2960474","volume":"5","author":"R Ju","year":"2021","unstructured":"Ju R, Zhou P, Wen S et al (2021) 3D-CNN-SPP: A patient risk prediction system from electronic health records via 3D CNN and Spatial pyramid Pooling[J]. IEEE Trans Emerg Top Comput Intell 5(2):247\u2013261. https:\/\/doi.org\/10.1109\/TETCI.2019.2960474","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"2","key":"1573_CR24","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1109\/TCCN.2019.2954388","volume":"6","author":"Y Lu","year":"2020","unstructured":"Lu Y, Liu L, Panneerselvam J et al (2020) A GRU-Based prediction framework for intelligent resource management at cloud data centres in the age of 5G[J]. IEEE Trans Cogn Commun Netw 6(2):486\u2013498. https:\/\/doi.org\/10.1109\/TCCN.2019.2954388","journal-title":"IEEE Trans Cogn Commun Netw"},{"issue":"8","key":"1573_CR25","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory[J]. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"1573_CR26","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","volume":"323","author":"A Sagheer","year":"2019","unstructured":"Sagheer A, Kotb M (2019) Time series forecasting of petroleum production using deep LSTM recurrent networks[J]. Neurocomputing 323:203\u2013213. https:\/\/doi.org\/10.1016\/j.neucom.2018.09.082","journal-title":"Neurocomputing"},{"key":"1573_CR27","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1016\/j.jhydrol.2018.04.065","volume":"561","author":"J Zhang","year":"2018","unstructured":"Zhang J, Zhu Y, Zhang X et al (2018) Developing a long Short-Term memory (LSTM) based model for predicting water table depth in agricultural areas[J]. J Hydrol 561:918\u2013929. https:\/\/doi.org\/10.1016\/j.jhydrol.2018.04.065","journal-title":"J Hydrol"},{"issue":"1","key":"1573_CR28","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","volume":"10","author":"W Kong","year":"2019","unstructured":"Kong W, Dong ZY, Jia Y et al (2019) Short-Term residential load forecasting based on LSTM Recurr-ent neural Network[J]. IEEE Trans Smart Grid 10(1):841\u2013851. https:\/\/doi.org\/10.1109\/TSG.2017.2753802","journal-title":"IEEE Trans Smart Grid"},{"issue":"5","key":"1573_CR29","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1109\/JAS.2021.1003976","volume":"8","author":"X Hou","year":"2021","unstructured":"Hou X, Wang K, Zhong C et al (2021) ST-Trader: A Spatial-Temporal deep neural network for Modelin-g stock market Movement[J]. IEEE\/CAA J Automatica Sinica 8(5):1015\u20131024. https:\/\/doi.org\/10.1109\/JAS.2021.1003976","journal-title":"IEEE\/CAA J Automatica Sinica"},{"key":"1573_CR30","unstructured":"Vaswani A, Shazeer N, Parmar N et al Attention is All you Need[J]. Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017:6000\u20136010.https:\/\/arxiv.org\/abs\/1706.03762"},{"key":"1573_CR31","doi-asserted-by":"publisher","unstructured":"Zhou H, Zhang S, Peng J et al (2021) Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting: Proceedings of the AAAI Conference on Artificial Intelligence[C]. https:\/\/doi.org\/10.1609\/aaai.v35i12.17325","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"1573_CR32","unstructured":"Wu H, Xu J, Wang J et al (2021) Autoformer: decomposition Transformers with auto-correlation for long-term series forecasting: advances in neural information processing Systems[C], https:\/\/arxiv.org\/abs\/2106.13008"},{"key":"1573_CR33","doi-asserted-by":"publisher","unstructured":"Liu S, Yu H, Liao C et al (2021) Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. Int Conf Learn Representations C. https:\/\/doi.org\/10.34726\/2945","DOI":"10.34726\/2945"},{"key":"1573_CR34","unstructured":"Liu Y, Wu H, Wang J et al (2022) Non-stationary transformers: exploring the stationarity in time series forecasting: advances in neural information processing Systems[C],.https:\/\/arxiv.org\/abs\/2205.14415"},{"key":"1573_CR35","unstructured":"Zhou T, Ma Z, Wen Q et al (2022) FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting: International Conference on Machine Learning[C]. https:\/\/arxiv.org\/abs\/2201.12740"},{"issue":"2","key":"1573_CR36","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TSM.2022.3164578","volume":"35","author":"CY Hsu","year":"2022","unstructured":"Hsu CY, Lu YW, Yan JH (2022) Temporal Convolution-Based Long-Short term memory network with attention mechanism for remaining useful life Prediction[J]. IEEE Trans Semicond Manuf 35(2):220\u2013228. https:\/\/doi.org\/10.1109\/TSM.2022.3164578","journal-title":"IEEE Trans Semicond Manuf"},{"issue":"3","key":"1573_CR37","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1109\/TCSS.2022.3158318","volume":"10","author":"Y Xie","year":"2023","unstructured":"Xie Y, Liu G, Yan C et al (2023) Time-Aware Attention-Based gated network for credit card fraud detection by extracting transactional Behaviors[J]. IEEE Trans Comput Social Syst 10(3):1004\u20131016. https:\/\/doi.org\/10.1109\/TCSS.2022.3158318","journal-title":"IEEE Trans Comput Social Syst"},{"issue":"6","key":"1573_CR38","doi-asserted-by":"publisher","first-page":"3699","DOI":"10.1109\/TSMC.2019.2932913","volume":"51","author":"J Chen","year":"2021","unstructured":"Chen J, Zhang J, Xu X et al (2021) E-LSTM-D: A deep learning framework for dynamic network link Prediction[J]. IEEE Trans Syst Man Cybernetics: Syst 51(6):3699\u20133712. https:\/\/doi.org\/10.1109\/TSMC.2019.2932913","journal-title":"IEEE Trans Syst Man Cybernetics: Syst"},{"issue":"5","key":"1573_CR39","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1109\/TCYB.2019.2945999","volume":"51","author":"X Xu","year":"2021","unstructured":"Xu X, Yoneda M (2021) Multitask Air-Quality prediction based on LSTM-Autoencoder Model[J]. IEEE Trans Cybernetics 51(5):2577\u20132586. https:\/\/doi.org\/10.1109\/TCYB.2019.2945999","journal-title":"IEEE Trans Cybernetics"},{"issue":"1","key":"1573_CR40","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1002\/qre.3651","volume":"41","author":"Y Yuan","year":"2025","unstructured":"Yuan Y, Yang Q, Wang G et al (2025) Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine[J]. Qual Reliab Eng Int 41(1):174\u2013191. https:\/\/doi.org\/10.1002\/qre.3651","journal-title":"Qual Reliab Eng Int"},{"issue":"3","key":"1573_CR41","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.apm.2018.01.014","volume":"62","author":"K Dragomiretskiy","year":"2014","unstructured":"Dragomiretskiy K, Zosso D (2014) Variational mode Decomposition[J]. IEEE Trans Signal Process 62(3):531\u2013544. https:\/\/doi.org\/10.1016\/j.apm.2018.01.014","journal-title":"IEEE Trans Signal Process"},{"key":"1573_CR42","unstructured":"Huang Z, Xu W, Yu K, Bidirectional LSTM-CRF Models for Sequence Tagging[J]. arXiv preprint arXiv:1508.01991, 2015. https:\/\/arxiv.org\/abs\/1508.01991"},{"key":"1573_CR43","doi-asserted-by":"publisher","first-page":"104913","DOI":"10.1016\/j.dsp.2024.104913","volume":"161","author":"Z Huang","year":"2025","unstructured":"Huang Z, Liu J (2025) Adaptive polymorphic mode decomposition[J]. Digit Signal Proc 161:104913. https:\/\/doi.org\/10.1016\/j.dsp.2024.104913","journal-title":"Digit Signal Proc"},{"key":"1573_CR44","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s10462-024-10989-8","volume":"58","author":"X Song","year":"2025","unstructured":"Song X, Deng L, Wang H et al (2025) Deep learning-based time series forecasting[J]. Artif Intell Rev 58:23. https:\/\/doi.org\/10.1007\/s10462-024-10989-8","journal-title":"Artif Intell Rev"},{"key":"1573_CR45","unstructured":"Donta P, Kumar P et al (2025) Performance measurements in the AI-centric computing continuum systems[J]. ArXiv. abs\/2506.22884https:\/\/arxiv.org\/abs\/2506.22884"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-025-01573-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-025-01573-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-025-01573-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:50:07Z","timestamp":1762955407000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-025-01573-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,18]]},"references-count":45,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["1573"],"URL":"https:\/\/doi.org\/10.1007\/s00607-025-01573-1","relation":{"references":[{"id-type":"doi","id":"10.1016\/j.compstruc.2021.106570","asserted-by":"subject"}]},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"type":"print","value":"0010-485X"},{"type":"electronic","value":"1436-5057"}],"subject":[],"published":{"date-parts":[[2025,10,18]]},"assertion":[{"value":"9 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2025","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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"214"}}