{"id":"https://openalex.org/W3032823819","doi":"https://doi.org/10.3390/sym12060893","title":"El Ni\u00f1o Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition","display_name":"El Ni\u00f1o Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3032823819","doi":"https://doi.org/10.3390/sym12060893","mag":"3032823819"},"language":"en","primary_location":{"id":"doi:10.3390/sym12060893","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12060893","pdf_url":"https://www.mdpi.com/2073-8994/12/6/893/pdf?version=1591062154","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/12/6/893/pdf?version=1591062154","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101737699","display_name":"Yanan Guo","orcid":"https://orcid.org/0000-0001-9372-8276"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanan Guo","raw_affiliation_strings":["College of Computer, National University of Defense Technology, Changsha 410073, China","College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"],"affiliations":[{"raw_affiliation_string":"College of Computer, National University of Defense Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110714473","display_name":"Xiaoqun Cao","orcid":"https://orcid.org/0000-0002-6135-0712"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoqun Cao","raw_affiliation_strings":["College of Computer, National University of Defense Technology, Changsha 410073, China","College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"],"affiliations":[{"raw_affiliation_string":"College of Computer, National University of Defense Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049887116","display_name":"Bainian Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bainian Liu","raw_affiliation_strings":["College of Computer, National University of Defense Technology, Changsha 410073, China","College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"],"affiliations":[{"raw_affiliation_string":"College of Computer, National University of Defense Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041870748","display_name":"Kecheng Peng","orcid":"https://orcid.org/0000-0002-3800-271X"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kecheng Peng","raw_affiliation_strings":["College of Computer, National University of Defense Technology, Changsha 410073, China","College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"],"affiliations":[{"raw_affiliation_string":"College of Computer, National University of Defense Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5110714473"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":4.1098,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.94701779,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"12","issue":"6","first_page":"893","last_page":"893"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9776999950408936,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10255","display_name":"Oceanographic and Atmospheric Processes","score":0.9747999906539917,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.827051043510437},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7610809803009033},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6424184441566467},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6097479462623596},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5520551204681396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5478203296661377},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5194658637046814},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48908716440200806},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.48890653252601624},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.48889032006263733},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4840035140514374},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4759352207183838},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4742909073829651},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4643004536628723},{"id":"https://openalex.org/keywords/chaotic","display_name":"Chaotic","score":0.4495016634464264},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.42961958050727844},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36454975605010986},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22427824139595032}],"concepts":[{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.827051043510437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7610809803009033},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6424184441566467},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6097479462623596},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5520551204681396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5478203296661377},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5194658637046814},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48908716440200806},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.48890653252601624},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.48889032006263733},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4840035140514374},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4759352207183838},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4742909073829651},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4643004536628723},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.4495016634464264},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.42961958050727844},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36454975605010986},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22427824139595032},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym12060893","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12060893","pdf_url":"https://www.mdpi.com/2073-8994/12/6/893/pdf?version=1591062154","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:43896e9e33ef46adae06da722d5ddfc2","is_oa":true,"landing_page_url":"https://doaj.org/article/43896e9e33ef46adae06da722d5ddfc2","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 12, Iss 6, p 893 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/12/6/893/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/sym12060893","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym12060893","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12060893","pdf_url":"https://www.mdpi.com/2073-8994/12/6/893/pdf?version=1591062154","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","display_name":"Life below water","score":0.5}],"awards":[{"id":"https://openalex.org/G5992835423","display_name":null,"funder_award_id":"2018YFC1506704","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G692460039","display_name":null,"funder_award_id":"41475094","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3032823819.pdf","grobid_xml":"https://content.openalex.org/works/W3032823819.grobid-xml"},"referenced_works_count":72,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1486724449","https://openalex.org/W1635210704","https://openalex.org/W1736701665","https://openalex.org/W1882805135","https://openalex.org/W1973633221","https://openalex.org/W1985864871","https://openalex.org/W1997278287","https://openalex.org/W2007221293","https://openalex.org/W2013513194","https://openalex.org/W2020492592","https://openalex.org/W2029956212","https://openalex.org/W2044097773","https://openalex.org/W2045419467","https://openalex.org/W2046794274","https://openalex.org/W2048156030","https://openalex.org/W2053537687","https://openalex.org/W2057324758","https://openalex.org/W2095705004","https://openalex.org/W2101306537","https://openalex.org/W2114393554","https://openalex.org/W2120390927","https://openalex.org/W2138315301","https://openalex.org/W2138698721","https://openalex.org/W2140603473","https://openalex.org/W2147569451","https://openalex.org/W2156693327","https://openalex.org/W2161565164","https://openalex.org/W2162876584","https://openalex.org/W2167623001","https://openalex.org/W2176502614","https://openalex.org/W2402144811","https://openalex.org/W2573279151","https://openalex.org/W2599047341","https://openalex.org/W2608334476","https://openalex.org/W2619995677","https://openalex.org/W2743680082","https://openalex.org/W2762689859","https://openalex.org/W2767264170","https://openalex.org/W2769156605","https://openalex.org/W2769677458","https://openalex.org/W2790643087","https://openalex.org/W2799074384","https://openalex.org/W2799355604","https://openalex.org/W2803464575","https://openalex.org/W2883361486","https://openalex.org/W2893221414","https://openalex.org/W2895418405","https://openalex.org/W2897169932","https://openalex.org/W2897470801","https://openalex.org/W2899934327","https://openalex.org/W2944278636","https://openalex.org/W2953118818","https://openalex.org/W2962894061","https://openalex.org/W2963211739","https://openalex.org/W2963547740","https://openalex.org/W2963746589","https://openalex.org/W2984274991","https://openalex.org/W2990969972","https://openalex.org/W2998317772","https://openalex.org/W3010349954","https://openalex.org/W3010758682","https://openalex.org/W3101723138","https://openalex.org/W3124661333","https://openalex.org/W3182762566","https://openalex.org/W4299429830","https://openalex.org/W6639172475","https://openalex.org/W6674330103","https://openalex.org/W6683710555","https://openalex.org/W6754614230","https://openalex.org/W6770530637","https://openalex.org/W6794811994"],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W2390914021","https://openalex.org/W2389417819"],"abstract_inverted_index":{"El":[0,40,111,137,173],"Ni\u00f1o":[1,41,112,138,174],"is":[2,54,67,141,244],"an":[3],"important":[4],"quasi-cyclical":[5],"climate":[6],"phenomenon":[7],"that":[8,48,267],"can":[9,78],"have":[10],"a":[11,87,118,161],"significant":[12,95],"impact":[13],"on":[14,228],"ecosystems":[15],"and":[16,26,59,73,99,129,163,187,200,223,257,282],"societies.":[17],"Due":[18],"to":[19,37,108,169],"the":[20,24,80,84,110,135,151,157,166,171,197,203,207,220,229,235,238,241,250,268,285],"chaotic":[21],"nature":[22],"of":[23,83,209,237,271],"atmosphere":[25],"ocean":[27],"systems,":[28],"traditional":[29,251],"methods":[30,248],"(such":[31],"as":[32,182],"statistical":[33,252],"methods)":[34],"are":[35,273],"difficult":[36],"provide":[38],"accurate":[39,277],"index":[42,113,139],"predictions.":[43],"The":[44,176,189,263],"latest":[45],"research":[46],"shows":[47],"Ensemble":[49],"Empirical":[50],"Mode":[51,147],"Decomposition":[52],"(EEMD)":[53],"suitable":[55],"for":[56,185],"analyzing":[57],"non-linear":[58],"non-stationary":[60],"signal":[61],"sequences,":[62],"Convolutional":[63],"Neural":[64,75],"Network":[65,76],"(CNN)":[66],"good":[68],"at":[69],"local":[70],"feature":[71],"extraction,":[72],"Recurrent":[74],"(RNN)":[77],"capture":[79],"overall":[81],"information":[82,218],"sequence.":[85],"As":[86],"special":[88],"RNN,":[89],"Long":[90],"Short-Term":[91],"Memory":[92],"(LSTM)":[93],"has":[94],"advantages":[96],"in":[97],"processing":[98],"predicting":[100],"long,":[101],"complex":[102],"time":[103,178,198,204,221,231],"series.":[104,232],"In":[105,131],"this":[106,132,212],"paper,":[107],"predict":[109],"more":[114,276,280],"accurately,":[115],"we":[116,155,164,215],"propose":[117],"new":[119,172],"hybrid":[120,133],"neural":[121,260,287],"network":[122,261,288],"model,":[123,134,240,253,256],"EEMD-CNN-LSTM,":[124],"which":[125,194],"combines":[126],"EEMD,":[127],"CNN,":[128],"LSTM.":[130,188],"original":[136],"sequence":[140],"first":[142,195],"decomposed":[143],"into":[144],"several":[145],"Intrinsic":[146],"Functions":[148],"(IMFs)":[149],"using":[150],"EEMD":[152],"method.":[153],"Next,":[154],"filter":[156],"IMFs":[158,168],"by":[159],"setting":[160],"threshold,":[162],"use":[165],"filtered":[167],"reconstruct":[170],"data.":[175],"reconstructed":[177,230],"series":[179,199,222],"then":[180,201,224],"serves":[181],"input":[183],"data":[184,191],"CNN":[186],"above":[190],"preprocessing":[192],"method,":[193],"decomposes":[196],"reconstructs":[202],"series,":[205],"uses":[206],"idea":[208],"symmetry.":[210],"With":[211],"symmetric":[213],"operation,":[214],"extract":[216],"valid":[217],"about":[219],"make":[225],"predictions":[226],"based":[227],"To":[233],"evaluate":[234],"performance":[236],"EEMD-CNN-LSTM":[239,272],"proposed":[242],"model":[243],"compared":[245],"with":[246],"four":[247],"including":[249],"machine":[254],"learning":[255],"other":[258],"deep":[259],"models.":[262],"experimental":[264],"results":[265,270],"show":[266],"prediction":[269],"not":[274],"only":[275],"but":[278],"also":[279],"stable":[281],"reliable":[283],"than":[284],"general":[286],"model.":[289]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
