iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.openalex.org/works/doi:10.1002/QRE.2098
{"id":"https://openalex.org/W2550310750","doi":"https://doi.org/10.1002/qre.2098","title":"A Hybrid Model Based on Singular Spectrum Analysis and Support Vector Machines Regression for Failure Time Series Prediction","display_name":"A Hybrid Model Based on Singular Spectrum Analysis and Support Vector Machines Regression for Failure Time Series Prediction","publication_year":2016,"publication_date":"2016-11-15","ids":{"openalex":"https://openalex.org/W2550310750","doi":"https://doi.org/10.1002/qre.2098","mag":"2550310750"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1002/qre.2098","pdf_url":null,"source":{"id":"https://openalex.org/S165633816","display_name":"Quality and Reliability Engineering International","issn_l":"0748-8017","issn":["0748-8017","1099-1638"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100327921","display_name":"Xin Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010193820","display_name":"Ji Wu","orcid":"https://orcid.org/0000-0002-5511-5361"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ji Wu","raw_affiliation_strings":["School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100447879","display_name":"Chao Liu","orcid":"https://orcid.org/0000-0002-3997-1758"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Liu","raw_affiliation_strings":["School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035708362","display_name":"Senzhang Wang","orcid":"https://orcid.org/0000-0002-3615-4859"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Senzhang Wang","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088602169","display_name":"Wensheng Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I11838497","display_name":"Aviation Industry Corporation of China (China)","ror":"https://ror.org/02wq41p38","country_code":"CN","type":"company","lineage":["https://openalex.org/I11838497"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wensheng Niu","raw_affiliation_strings":["Aeronautical Computing Technique Research Institute, Aviation Industry Corporation of China, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Aeronautical Computing Technique Research Institute, Aviation Industry Corporation of China, Xi'an, China","institution_ids":["https://openalex.org/I11838497"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010193820"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":{"value":4330,"currency":"USD","value_usd":4330,"provenance":"doaj"},"apc_paid":null,"fwci":4.213,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":22,"citation_normalized_percentile":{"value":0.946839,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":92,"max":93},"biblio":{"volume":"32","issue":"8","first_page":"2717","last_page":"2738"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T13487","display_name":"Total Least Squares Methods and Applications","score":0.9993,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13487","display_name":"Total Least Squares Methods and Applications","score":0.9993,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14369","display_name":"Meta-Synthesis Approach to Knowledge Science and Innovation","score":0.9654,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12368","display_name":"Application of Grey System Theory in Forecasting","score":0.9566,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/singular-spectrum-analysis","display_name":"Singular Spectrum Analysis","score":0.540099},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.528363},{"id":"https://openalex.org/keywords/errors-in-variables-models","display_name":"Errors-in-Variables Models","score":0.523608},{"id":"https://openalex.org/keywords/forecasting-model-optimization","display_name":"Forecasting Model Optimization","score":0.5137}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.63017875},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.61631405},{"id":"https://openalex.org/C136272165","wikidata":"https://www.wikidata.org/wiki/Q4048889","display_name":"Singular spectrum analysis","level":3,"score":0.61247444},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.57587314},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5315321},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5093726},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47307405},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.458917},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.41121292},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3252905},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29684493},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.29177308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27829915},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16827166},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13908511},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1002/qre.2098","pdf_url":null,"source":{"id":"https://openalex.org/S165633816","display_name":"Quality and Reliability Engineering International","issn_l":"0748-8017","issn":["0748-8017","1099-1638"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.41}],"grants":[{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"61602237"}],"datasets":[],"versions":[],"referenced_works_count":56,"referenced_works":["https://openalex.org/W1536447791","https://openalex.org/W1543905881","https://openalex.org/W1578517626","https://openalex.org/W1604938182","https://openalex.org/W1908079346","https://openalex.org/W1965995547","https://openalex.org/W197787652","https://openalex.org/W1983732626","https://openalex.org/W1990785420","https://openalex.org/W1997747134","https://openalex.org/W2007221293","https://openalex.org/W2016864600","https://openalex.org/W2022754289","https://openalex.org/W2025727209","https://openalex.org/W2028465659","https://openalex.org/W2033412785","https://openalex.org/W2038097457","https://openalex.org/W2039238531","https://openalex.org/W2042230250","https://openalex.org/W2042937152","https://openalex.org/W2045256553","https://openalex.org/W2049387654","https://openalex.org/W2057018326","https://openalex.org/W2058628428","https://openalex.org/W2061879449","https://openalex.org/W2067317819","https://openalex.org/W2069508080","https://openalex.org/W2078282181","https://openalex.org/W2079541639","https://openalex.org/W2085591950","https://openalex.org/W2088023283","https://openalex.org/W2090785896","https://openalex.org/W2092673937","https://openalex.org/W2093230975","https://openalex.org/W2096684483","https://openalex.org/W2101156118","https://openalex.org/W2116512828","https://openalex.org/W2121460124","https://openalex.org/W2139386984","https://openalex.org/W2144556677","https://openalex.org/W2144717556","https://openalex.org/W2149541376","https://openalex.org/W2153667946","https://openalex.org/W2156397254","https://openalex.org/W2156909104","https://openalex.org/W2158001550","https://openalex.org/W2158994553","https://openalex.org/W2172064003","https://openalex.org/W2181932477","https://openalex.org/W2479224257","https://openalex.org/W2578871088","https://openalex.org/W2584422723","https://openalex.org/W2962891490","https://openalex.org/W3017996965","https://openalex.org/W3125327113","https://openalex.org/W4232614556"],"related_works":["https://openalex.org/W4387300193","https://openalex.org/W4312561791","https://openalex.org/W4220770032","https://openalex.org/W3201477101","https://openalex.org/W3175321409","https://openalex.org/W2974356760","https://openalex.org/W2795171006","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W2024695206"],"abstract_inverted_index":{"Effectively":[0],"forecasting":[1,146],"the":[2,6,36,48,73,80,84,93,100,108,116,122,141,147,151,155,170,207],"failure":[3,74,102],"data":[4,77,105,192],"in":[5,11],"maintenance":[7,81],"stage":[8,82],"is":[9,40,173],"essential":[10],"many":[12],"reliability":[13,37,222],"planning":[14],"and":[15,32,65,95,112,115,124,134,145,195,213],"scheduling":[16],"activities.":[17],"Although":[18],"a":[19,56,162,218],"number":[20],"of":[21,83,110,143,154,169,191],"data\u2010driven":[22],"techniques":[23,109,212],"have":[24],"been":[25],"applied":[26],"to":[27,71,139],"cope":[28],"with":[29,121],"this":[30,52],"issue":[31],"achieved":[33],"noteworthy":[34],"performance,":[35],"prediction":[38],"problem":[39],"still":[41],"not":[42],"fully":[43],"explored,":[44],"especially":[45],"for":[46,221],"applying":[47],"hybridization":[49],"methods.":[50],"In":[51],"paper,":[53],"we":[54],"introduce":[55],"hybrid":[57,156],"model":[58,157,172,209],"which":[59],"integrates":[60],"singular":[61],"spectrum":[62],"analysis":[63],"(SSA)":[64],"support":[66],"vector":[67],"machines":[68],"regression":[69],"(SVR)":[70],"forecast":[72,223],"time":[75,103],"series":[76,104],"gathered":[78],"from":[79,99],"Boeing":[85],"737":[86],"aircraft.":[87],"Two":[88],"significant":[89],"components":[90,118],"recognized":[91],"as":[92,132,180,197,199,217],"trend":[94],"fluctuation":[96],"are":[97,119,136,158],"extracted":[98],"original":[101],"by":[106,161],"using":[107],"SSA":[111],"noise":[113],"test,":[114],"two":[117,129,148],"associated":[120],"inherent":[123],"operational":[125],"reliability,":[126],"respectively.":[127],"Then":[128],"models":[130,178],"named":[131],"trend\u2010SSA":[133],"fluctuation\u2010SVR":[135],"individually":[137],"developed":[138],"conduct":[140],"tasks":[142],"modeling":[144],"components.":[149],"Furthermore,":[150],"optimal":[152],"parameters":[153],"obtained":[159],"efficiently":[160],"stepwise":[163],"grid":[164],"search":[165],"method.":[166],"The":[167,202],"performance":[168],"presented":[171],"measured":[174],"against":[175],"other":[176,211],"unitary":[177],"such":[179],"Holt\u2010Winters,":[181],"autoregressive":[182],"integrated":[183],"moving":[184],"average,":[185],"multiple":[186],"linear":[187],"regression,":[188],"group":[189],"method":[190],"handling,":[193],"SSA,":[194],"SVR,":[196],"well":[198],"their":[200],"hybridizations.":[201],"comparison":[203],"results":[204],"indicate":[205],"that":[206],"proposed":[208],"outperforms":[210],"can":[214],"be":[215],"utilized":[216],"promising":[219],"tool":[220],"applications.":[224],"Copyright":[225],"\u00a9":[226],"2016":[227],"John":[228],"Wiley":[229],"&":[230],"Sons,":[231],"Ltd.":[232]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2550310750","counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2024-11-18T03:41:51.136749","created_date":"2016-11-30"}