{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T08:00:34Z","timestamp":1764403234107,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Science and Technology Innovation Talent Project of Sichuan Province","award":["Grant No. 2021JDRC0012"],"award-info":[{"award-number":["Grant No. 2021JDRC0012"]}]},{"name":"Independent Research Project of National Key Laboratory of Traction Power of China","award":["Grant No. 2019TPL-T19"],"award-info":[{"award-number":["Grant No. 2019TPL-T19"]}]},{"name":"Key Interdisciplinary Basic Research Project of Southwest Jiaotong University","award":["Grant No. 2682021ZTPY089"],"award-info":[{"award-number":["Grant No. 2682021ZTPY089"]}]},{"name":"Open Research Project of National Rail Transit Electrification and Automation Engineering Technology Research Center","award":["Grant No. NEEC-2019-B06"],"award-info":[{"award-number":["Grant No. NEEC-2019-B06"]}]},{"name":"State Scholarship Fund of China Scholarship Council","award":["Grant No. 202007000101"],"award-info":[{"award-number":["Grant No. 202007000101"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 52277127"],"award-info":[{"award-number":["Grant No. 52277127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11063-023-11199-z","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T18:02:52Z","timestamp":1677693772000},"page":"9225-9245","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Effective Ionospheric TEC Predicting Approach Using EEMD-PE-Kmeans and Self-Attention LSTM"],"prefix":"10.1007","volume":"55","author":[{"given":"Xingyu","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuemin","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7926-9501","authenticated-orcid":false,"given":"Wei","family":"Quan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiquan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guosong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"issue":"A9","key":"11199_CR1","doi-asserted-by":"publisher","first-page":"SMP 11-1","DOI":"10.1029\/2001JA000214","volume":"107","author":"RM Winglee","year":"2002","unstructured":"Winglee RM, Chua D, Brittnacher M, Parks GK, Lu G (2002) Global impact of ionospheric outflows on the dynamics of the magnetosphere and cross-polar cap potential. J Geophys Res-Space 107(A9):SMP 11-1-SMP 11-12","journal-title":"J Geophys Res-Space"},{"issue":"5","key":"11199_CR2","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1177\/15500594211064008","volume":"53","author":"FH \u00c7etin","year":"2022","unstructured":"\u00c7etin FH, Bar\u0131\u015f Usta M, Ayd\u0131n S, G\u00fcven AS (2022) A case study on EEG analysis: embedding entropy estimations indicate the decreased neuro-cortical complexity levels mediated by methylphenidate treatment in children with ADHD. Clin EEG Neurosci 53(5):406\u2013417","journal-title":"Clin EEG Neurosci"},{"key":"11199_CR3","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.neulet.2018.11.034","volume":"694","author":"S Ayd\u0131n","year":"2019","unstructured":"Ayd\u0131n S, G\u00fcd\u00fcc\u00fc \u00c7, Kutluk F, \u00d6niz A (2019) The impact of musical experience on neural sound encoding performance. Neurosci Lett 694:124\u2013128","journal-title":"Neurosci Lett"},{"issue":"20","key":"11199_CR4","doi-asserted-by":"publisher","first-page":"7144","DOI":"10.3390\/app10207144","volume":"10","author":"A Mengarelli","year":"2020","unstructured":"Mengarelli A, Tigrini A, Fioretti S, Cardarelli S, Verdini F (2020) On the use of fuzzy and permutation entropy in hand gesture characterization from EMG signals: parameters selection and comparison. Appl Sci 10(20):7144","journal-title":"Appl Sci"},{"key":"11199_CR5","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.compbiomed.2017.05.003","volume":"86","author":"CC Naranjo","year":"2017","unstructured":"Naranjo CC, Sanchez-Rodriguez LM, Mart\u00ednez MB, B\u00e1ez ME, Garc\u00eda AM (2017) Permutation entropy analysis of heart rate variability for the assessment of cardiovascular autonomic neuropathy in type 1 diabetes mellitus. Comput Biol Med 86:90\u201397","journal-title":"Comput Biol Med"},{"issue":"10","key":"11199_CR6","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.3390\/e21101001","volume":"21","author":"T Zhang","year":"2019","unstructured":"Zhang T, Cheng C, Gao P (2019) Permutation entropy-based analysis of temperature complexity spatial-temporal variation and its driving factors in China. Entropy 21(10):1001","journal-title":"Entropy"},{"key":"11199_CR7","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.ymssp.2017.06.011","volume":"99","author":"J Zheng","year":"2018","unstructured":"Zheng J, Pan H, Yang S, Cheng J (2018) Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis. Mech Syst Signal Process 99:229\u2013243","journal-title":"Mech Syst Signal Process"},{"issue":"2","key":"11199_CR8","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1029\/RG016i002p00177","volume":"16","author":"K Rawer","year":"1978","unstructured":"Rawer K, Bilitza D, Ramakrishnan S (1978) Goals and status of the international reference ionosphere. Rev Geophys 16(2):177\u2013181","journal-title":"Rev Geophys"},{"key":"11199_CR9","doi-asserted-by":"publisher","first-page":"A07","DOI":"10.1051\/swsc\/2014004","volume":"4","author":"D Bilitza","year":"2014","unstructured":"Bilitza D, Altadill D, Zhang Y, Mertens C (2014) The international reference ionosphere 2012: a model of international collaboration. J Space Weather Space 4:A07","journal-title":"J Space Weather Space"},{"issue":"2","key":"11199_CR10","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1002\/2016SW001593","volume":"15","author":"D Bilitza","year":"2017","unstructured":"Bilitza D, Altadill D, Truhlik V, Galkin I, Reinisch B, Huang X (2017) International reference ionosphere 2016: from ionospheric climate to real-time weather predictions. Space Weather 15(2):418\u2013429","journal-title":"Space Weather"},{"key":"11199_CR11","doi-asserted-by":"crossref","unstructured":"Li X, Guo D (2010) Modeling and prediction of ionospheric total electron content by time series analysis. In: 2010 2nd international conference on advanced computer control. IEEE, pp 375\u2013379","DOI":"10.1109\/ICACC.2010.5486653"},{"issue":"3","key":"11199_CR12","first-page":"267","volume":"36","author":"P Chen","year":"2011","unstructured":"Chen P, Yao Y, Wu H (2011) TEC prediction of ionosphere based on time series analysis. Geomat Inf Sci Wuhan Univ 36(3):267\u2013270","journal-title":"Geomat Inf Sci Wuhan Univ"},{"key":"11199_CR13","first-page":"331","volume":"41","author":"T Lu","year":"2021","unstructured":"Lu T, Huang J, Lu C (2021) Short-term lonospheric TEC prediction model based on EWT-ARMA. J Geod Geodyn 41:331\u2013335","journal-title":"J Geod Geodyn"},{"issue":"1","key":"11199_CR14","first-page":"9","volume":"2","author":"X Zhang","year":"2020","unstructured":"Zhang X, Ren X, Wu F, Lu Q (2020) Short-term prediction of ionospheric TEC based on ARIMA model. Acta Geod Cartogr Sin 2(1):9\u201316","journal-title":"Acta Geod Cartogr Sin"},{"key":"11199_CR15","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.5194\/isprs-archives-XLII-3-W10-1147-2020","volume":"42","author":"C Li","year":"2020","unstructured":"Li C, Peng H, Huang LK, Liu LL, Xie SF (2020) Research on short-term ionospheric prediction combining with EOF and ARIMA model over Guangxi Area. Int Arch Photogramm Remote Sens Spat Inf Sci 42:1147\u20131153","journal-title":"Int Arch Photogramm Remote Sens Spat Inf Sci"},{"issue":"3","key":"11199_CR16","first-page":"610","volume":"43","author":"T Rui","year":"2021","unstructured":"Rui T, Xurong D (2021) Ionosphere VTEC prediction model fused with wavelet decomposition and Prophet framework. J Syst Eng Electron 43(3):610\u2013622","journal-title":"J Syst Eng Electron"},{"issue":"2","key":"11199_CR17","doi-asserted-by":"publisher","first-page":"173","DOI":"10.5194\/angeo-31-173-2013","volume":"31","author":"M Akhoondzadeh","year":"2013","unstructured":"Akhoondzadeh M (2013) Support vector machines for TEC seismo-ionospheric anomalies detection. Ann Geophys 31(2):173\u2013186","journal-title":"Ann Geophys"},{"issue":"4","key":"11199_CR18","first-page":"395","volume":"40","author":"J Tang","year":"2020","unstructured":"Tang J, Gao X (2020) Prediction models of ionospheric TEC by MEEMD and Elman recurrent neural network. J Geod Geodyn 40(4):395\u2013399","journal-title":"J Geod Geodyn"},{"issue":"4","key":"11199_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10509-019-3545-9","volume":"364","author":"S Inyurt","year":"2019","unstructured":"Inyurt S, Sekertekin A (2019) Modeling and predicting seasonal ionospheric variations in Turkey using artificial neural network (ANN). Astrophys Space Sci 364(4):1\u20138","journal-title":"Astrophys Space Sci"},{"issue":"4","key":"11199_CR20","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/j.asr.2018.03.043","volume":"62","author":"R Song","year":"2018","unstructured":"Song R, Zhang X, Zhou C, Liu J, He J (2018) Predicting TEC in China based on the neural networks optimized by genetic algorithm. Adv Space Res 62(4):745\u2013759","journal-title":"Adv Space Res"},{"issue":"5","key":"11199_CR21","doi-asserted-by":"publisher","first-page":"857","DOI":"10.5194\/angeo-30-857-2012","volume":"30","author":"JB Habarulema","year":"2012","unstructured":"Habarulema JB, McKinnell LA (2012) Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data. Ann Geophys 30(5):857\u2013866","journal-title":"Ann Geophys"},{"issue":"4","key":"11199_CR22","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1002\/2013RS005247","volume":"49","author":"Z Huang","year":"2014","unstructured":"Huang Z, Yuan H (2014) Ionospheric single-station TEC short-term forecast using RBF neural network. Radio Sci 49(4):283\u2013292","journal-title":"Radio Sci"},{"issue":"1","key":"11199_CR23","doi-asserted-by":"publisher","first-page":"48","DOI":"10.11728\/cjss2018.01.048","volume":"38","author":"T Yuan","year":"2018","unstructured":"Yuan T, Chen Y, Liu S, Gong J (2018) Prediction model for ionospheric total electron content based on deep learning recurrent neural networkormalsize. Chin J Space Sci 38(1):48\u201357","journal-title":"Chin J Space Sci"},{"key":"11199_CR24","doi-asserted-by":"crossref","unstructured":"Sun W, Xu L, Huang X, Zhang W, Yuan T, Chen Z, Yan Y (2017) Forecasting of ionospheric vertical total electron content (TEC) using LSTM networks. In: 2017 international conference on machine learning and cybernetics (ICMLC). IEEE, pp 340\u2013344","DOI":"10.1109\/ICMLC.2017.8108945"},{"key":"11199_CR25","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, vol 27"},{"issue":"4","key":"11199_CR26","doi-asserted-by":"publisher","first-page":"316","DOI":"10.3390\/atmos11040316","volume":"11","author":"R Tang","year":"2020","unstructured":"Tang R, Zeng F, Chen Z, Wang JS (2020) The comparison of predicting storm-time ionospheric TEC by three methods: ARIMA, LSTM, and Seq2Seq. Atmosphere 11(4):316","journal-title":"Atmosphere"},{"issue":"4","key":"11199_CR27","first-page":"553","volume":"36","author":"Z Fubin","year":"2021","unstructured":"Fubin Z, Chen Z, Cheng W, Jiaqi Z, Yi L, Guozhen X, Zhengyu Z (2021) Global ionospheric TEC prediction based on deep learning. Chin J Radio Sci 36(4):553\u2013561","journal-title":"Chin J Radio Sci"},{"issue":"6","key":"11199_CR28","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1109\/LGRS.2020.2992633","volume":"18","author":"A Ruwali","year":"2020","unstructured":"Ruwali A, Kumar AJS, Prakash KB, Sivavaraprasad G, Ratnam DV (2020) Implementation of hybrid deep learning model (LSTM-CNN) for ionospheric TEC forecasting using GPS data. IEEE Geosci Remote Sens Lett 18(6):1004\u20131008","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"11199_CR29","doi-asserted-by":"crossref","unstructured":"Yang X, Kang X, Guo C, Wu J (2020) Prediction of ionospheric total electron content based on deep learning. In: 2020 international conference on intelligent computing, automation and systems (ICICAS). IEEE, pp 220\u2013223.","DOI":"10.1109\/ICICAS51530.2020.00052"},{"key":"11199_CR30","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.neucom.2020.01.031","volume":"388","author":"C Fan","year":"2020","unstructured":"Fan C, Ding C, Zheng J, Xiao L, Ai Z (2020) Empirical mode decomposition based multi-objective deep belief network for short-term power load forecasting. Neurocomputing 388:110\u2013123","journal-title":"Neurocomputing"},{"key":"11199_CR31","first-page":"1","volume":"70","author":"R Guo","year":"2021","unstructured":"Guo R, Wang Y, Zhang H, Zhang G (2021) Remaining useful life prediction for rolling bearings using EMD-RISI-LSTM. IEEE Trans Instrum Meas 70:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"11199_CR32","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1109\/JSYST.2020.3007184","volume":"15","author":"D Kim","year":"2020","unstructured":"Kim D, Kwon D, Park L, Kim J, Cho S (2020) Multiscale LSTM-based deep learning for very-short-term photovoltaic power generation forecasting in smart city energy management. IEEE Syst J 15(1):346\u2013354","journal-title":"IEEE Syst J"},{"issue":"12","key":"11199_CR33","doi-asserted-by":"publisher","first-page":"4296","DOI":"10.1007\/s10489-020-01814-0","volume":"50","author":"H Niu","year":"2020","unstructured":"Niu H, Xu K, Wang W (2020) A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network. Appl Intell 50(12):4296\u20134309","journal-title":"Appl Intell"},{"issue":"3","key":"11199_CR34","doi-asserted-by":"publisher","first-page":"4614","DOI":"10.1109\/JSYST.2019.2961172","volume":"14","author":"O Abedinia","year":"2020","unstructured":"Abedinia O, Bagheri M, Naderi MS, Ghadimi N (2020) A new combinatory approach for wind power forecasting. IEEE Syst J 14(3):4614\u20134625","journal-title":"IEEE Syst J"},{"issue":"01","key":"11199_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S1793536909000047","volume":"1","author":"Z Wu","year":"2009","unstructured":"Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(01):1\u201341","journal-title":"Adv Adapt Data Anal"},{"issue":"17","key":"11199_CR36","doi-asserted-by":"publisher","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","volume":"88","author":"C Bandt","year":"2002","unstructured":"Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88(17):174102","journal-title":"Phys Rev Lett"},{"key":"11199_CR37","doi-asserted-by":"crossref","unstructured":"Luong MT, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025.","DOI":"10.18653\/v1\/D15-1166"},{"issue":"3","key":"11199_CR38","first-page":"277","volume":"13","author":"YW Cheung","year":"1995","unstructured":"Cheung YW, Lai KS (1995) Lag order and critical values of the augmented Dickey\u2013Fuller test. J Bus Econ Stat 13(3):277\u2013280","journal-title":"J Bus Econ Stat"},{"key":"11199_CR39","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11199-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-023-11199-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11199-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T17:08:17Z","timestamp":1699722497000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-023-11199-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,1]]},"references-count":39,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11199"],"URL":"https:\/\/doi.org\/10.1007\/s11063-023-11199-z","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2023,3,1]]},"assertion":[{"value":"16 February 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2023","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}