{"id":"https://openalex.org/W2200020869","doi":"https://doi.org/10.1109/bigdata.2015.7363846","title":"An ensemble learning based approach for building airfare forecast service","display_name":"An ensemble learning based approach for building airfare forecast service","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2200020869","doi":"https://doi.org/10.1109/bigdata.2015.7363846","mag":"2200020869"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2015.7363846","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363846","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100428118","display_name":"Yuwen Chen","orcid":"https://orcid.org/0000-0002-4143-7116"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuwen Chen","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100648774","display_name":"Jian Cao","orcid":"https://orcid.org/0000-0002-0036-9436"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Cao","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086115549","display_name":"Shanshan Feng","orcid":"https://orcid.org/0000-0002-6161-9232"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanshan Feng","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103818981","display_name":"Yudong Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210086566","display_name":"Ctrip","ror":"https://ror.org/003k32q32","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210086566"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yudong Tan","raw_affiliation_strings":["Ctrip.com, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ctrip.com, Shanghai, China","institution_ids":["https://openalex.org/I4210086566"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"964","last_page":"969"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9973000288009644,"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"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9973000288009644,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.996999979019165,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7742767333984375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5670639872550964},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.5439295172691345},{"id":"https://openalex.org/keywords/schema","display_name":"Schema (genetic algorithms)","score":0.5315456390380859},{"id":"https://openalex.org/keywords/revenue","display_name":"Revenue","score":0.5237236618995667},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.4853014349937439},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47373712062835693},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.47202152013778687},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.44660714268684387},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4418126940727234},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.42695170640945435},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.36840924620628357},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3536812663078308},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.2604396343231201},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.10650253295898438},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08495348691940308}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7742767333984375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5670639872550964},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.5439295172691345},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.5315456390380859},{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.5237236618995667},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4853014349937439},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47373712062835693},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.47202152013778687},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.44660714268684387},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4418126940727234},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.42695170640945435},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.36840924620628357},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3536812663078308},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.2604396343231201},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.10650253295898438},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08495348691940308},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2015.7363846","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363846","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1547333707","https://openalex.org/W1571836963","https://openalex.org/W1974961444","https://openalex.org/W1994518858","https://openalex.org/W2014928429","https://openalex.org/W2024462882","https://openalex.org/W2028072219","https://openalex.org/W2047028564","https://openalex.org/W2101234009","https://openalex.org/W2123003172","https://openalex.org/W2139015071","https://openalex.org/W2160218441","https://openalex.org/W2160512933","https://openalex.org/W2165799067","https://openalex.org/W2171809276","https://openalex.org/W2172064003","https://openalex.org/W2330820318","https://openalex.org/W2537393548","https://openalex.org/W3009009611","https://openalex.org/W3135189827","https://openalex.org/W4234173777","https://openalex.org/W6675354045","https://openalex.org/W6683584131","https://openalex.org/W6685747873","https://openalex.org/W6791310677"],"related_works":["https://openalex.org/W2990081132","https://openalex.org/W4296984035","https://openalex.org/W3127121676","https://openalex.org/W3108206468","https://openalex.org/W2068319486","https://openalex.org/W4389000576","https://openalex.org/W3160499573","https://openalex.org/W2810128799","https://openalex.org/W2619158163","https://openalex.org/W2167825284"],"abstract_inverted_index":{"Modern":[0],"airlines":[1],"use":[2],"sophisticated":[3],"pricing":[4],"models":[5],"to":[6,26,40,63,129,163,205],"maximize":[7],"their":[8],"revenue,":[9],"which":[10,52],"results":[11],"in":[12,51,152],"highly":[13],"volatile":[14],"airfares.":[15],"Without":[16],"sufficient":[17],"information,":[18],"it":[19],"is":[20,56,126,168],"usually":[21],"difficult":[22],"for":[23,48,150],"ordinary":[24],"customers":[25],"estimate":[27],"future":[28],"price":[29,75,131,175,193],"changes.":[30],"Over":[31],"the":[32,42,53,73,83,91,103,138,160,188],"last":[33],"few":[34],"years,":[35],"several":[36],"studies":[37],"have":[38],"tried":[39],"solve":[41],"problem":[43,92],"of":[44,93,107,159,170,181],"optimal":[45],"purchase":[46],"timing":[47],"flight":[49],"tickets,":[50],"prediction":[54,119,139],"task":[55],"described":[57],"as":[58],"a":[59,68,98,109,157,165],"binary":[60],"classification":[61],"concerning":[62],"buy":[64],"or":[65],"wait":[66],"at":[67],"given":[69],"point.":[70],"However,":[71],"forecasting":[72],"real-time":[74],"changes":[76],"has":[77],"never":[78],"received":[79],"much":[80],"attention":[81],"from":[82,173,196],"research":[84],"community.":[85],"In":[86],"this":[87],"paper,":[88],"we":[89,141],"address":[90],"airfare":[94],"forecast":[95,110,115,189],"and":[96,117,133,199,207],"present":[97],"systematic":[99],"approach":[100],"that":[101,167],"covers":[102],"most":[104],"important":[105],"aspects":[106],"building":[108],"service,":[111],"including":[112],"data":[113,124,194],"modelling,":[114],"algorithm":[116,162],"long-term":[118,208],"strategies.":[120],"A":[121],"novel":[122],"matrix-like":[123],"schema":[125],"first":[127],"introduced":[128],"organize":[130],"series":[132],"extract":[134],"temporal":[135],"features.":[136],"For":[137],"task,":[140],"specifically":[142],"investigate":[143],"Learn++.NSE,":[144],"an":[145,178],"incremental":[146],"ensemble":[147],"classifier":[148],"designed":[149],"learning":[151,171],"nonstationary":[153],"environments.":[154],"We":[155,185],"propose":[156],"modification":[158],"original":[161],"make":[164],"regressor":[166],"capable":[169],"incrementally":[172],"streaming":[174],"series,":[176],"with":[177,203],"extra":[179],"ability":[180],"multi-step":[182],"ahead":[183],"forecasting.":[184],"further":[186],"evaluate":[187],"model":[190],"on":[191],"real-world":[192],"collected":[195],"diverse":[197],"routes":[198],"discuss":[200],"its":[201],"performance":[202],"respect":[204],"short-term":[206],"prediction.":[209]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}