{"id":"https://openalex.org/W7140200630","doi":"https://doi.org/10.1016/j.procs.2026.03.002","title":"Forecasting Mechanisms for Habitational Tourism","display_name":"Forecasting Mechanisms for Habitational Tourism","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140200630","doi":"https://doi.org/10.1016/j.procs.2026.03.002"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2026.03.002","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2026.03.002","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2026.03.002","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jos\u00e9 P.R. Rocha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jos\u00e9 P.R. Rocha","raw_affiliation_strings":["EST - School of Technology, IPCA, Barcelos, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EST - School of Technology, IPCA, Barcelos, Portugal","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Maria C.S. Lima","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maria C.S. Lima","raw_affiliation_strings":["TURIHAB - Associa\u00e7\u00e3o do Turismo de Habita\u00e7\u00e3o, Ponte de Lima, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TURIHAB - Associa\u00e7\u00e3o do Turismo de Habita\u00e7\u00e3o, Ponte de Lima, Portugal","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Lu\u00eds G.M. Ferreira","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lu\u00eds G.M. Ferreira","raw_affiliation_strings":["2Ai - School of Technology, IPCA, Barcelos, Portugal, LASI \u2013 Associate Laboratory of Intelligent"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"2Ai - School of Technology, IPCA, Barcelos, Portugal, LASI \u2013 Associate Laboratory of Intelligent","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.62496744,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"278","issue":null,"first_page":"362","last_page":"371"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.2955000102519989,"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":0.2955000102519989,"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/T10055","display_name":"Diverse Aspects of Tourism Research","score":0.1899999976158142,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.02800000086426735,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/tourism","display_name":"Tourism","score":0.8115000128746033},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.550000011920929},{"id":"https://openalex.org/keywords/occupancy","display_name":"Occupancy","score":0.5257999897003174},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5087000131607056},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5056999921798706},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.460999995470047},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.3874000012874603}],"concepts":[{"id":"https://openalex.org/C18918823","wikidata":"https://www.wikidata.org/wiki/Q49389","display_name":"Tourism","level":2,"score":0.8115000128746033},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7620999813079834},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.550000011920929},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.5257999897003174},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5087000131607056},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.44909998774528503},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.42410001158714294},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3874000012874603},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3596999943256378},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.29280000925064087},{"id":"https://openalex.org/C161657586","wikidata":"https://www.wikidata.org/wiki/Q1203326","display_name":"Technology forecasting","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.2621999979019165}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2026.03.002","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2026.03.002","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2026.03.002","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2026.03.002","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.7331264019012451,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1496283843","https://openalex.org/W2006746888","https://openalex.org/W2149045411","https://openalex.org/W2554640988","https://openalex.org/W2984637266","https://openalex.org/W3120375716","https://openalex.org/W3127948630","https://openalex.org/W3187517276","https://openalex.org/W3194077746","https://openalex.org/W4229046709","https://openalex.org/W4231261713","https://openalex.org/W4307047972"],"related_works":[],"abstract_inverted_index":{"Tourism":[0],"is":[1,180],"globally":[2],"accepted":[3],"as":[4,173],"one":[5],"of":[6,19,29,88,102,159,184],"the":[7,27,73,81,95,100,114,118,125,134,147,157,168,182,192,199],"largest":[8],"business":[9],"industries":[10],"that":[11,52,155],"can":[12],"easily":[13],"be":[14],"affected":[15],"by":[16],"a":[17,36,163,174],"variety":[18],"external":[20],"factors":[21],"and":[22,25,42,65,76,109,128,137,170,195],"events.":[23],"Assessing":[24],"anticipating":[26],"impact":[28],"certain":[30],"events":[31],"on":[32,133,190],"tourism":[33],"has":[34],"become":[35],"necessity":[37],"in":[38,113,143,181],"this":[39],"increasingly":[40],"dynamic":[41],"demanding":[43],"world.":[44],"According":[45],"to":[46,79,93,145],"TURIHAB,":[47],"there":[48],"are":[49],"no":[50,67],"mechanisms":[51],"allow":[53],"objective":[54],"forecasts":[55],"for":[56,116,124],"habitational":[57],"tourism,":[58],"namely":[59],"its":[60],"brands":[61],"Solares":[62],"de":[63],"Portugal":[64],"Casas":[66],"Campo":[68],".":[69],"This":[70],"paper":[71],"documents":[72],"techniques,":[74],"models":[75,105,194],"platforms":[77],"explored":[78,142],"develop":[80],"expected":[82],"forecast":[83,160],"mechanism.":[84],"The":[85,149,177],"arduous":[86],"process":[87,183],"obtaining":[89],"real":[90],"data":[91],"series":[92],"train":[94],"regression":[96],"model,":[97],"together":[98],"with":[99],"exploration":[101],"two":[103],"statistical":[104],"(":[106],"Random":[107],"Forest":[108],"SARIMAX":[110],")":[111],"resulted":[112,178],"tools":[115],"forecasting":[117],"TURIHAB":[119],"association\u2019s":[120],"monthly":[121],"occupancy":[122],"rates":[123],"years":[126],"2024":[127],"2025.":[129],"In":[130],"addition,":[131],"adjustments":[132],"training/test":[135],"sets":[136],"hyperparameter":[138],"tuning":[139],"techniques":[140],"were":[141],"order":[144],"improve":[146],"model.":[148],"performance":[150],"evaluation":[151],"followed":[152],"specific":[153],"metrics":[154],"fit":[156],"type":[158],"expected.":[161],"Finally,":[162],"comparison":[164],"was":[165],"made":[166],"between":[167],"predicted":[169],"actual":[171],"values":[172],"validation":[175],"strategy.":[176],"dataset":[179],"being":[185],"published.":[186],"For":[187],"any":[188],"interest":[189],"accessing":[191],"developed":[193],"used":[196],"datatset,":[197],"contact":[198],"authors.":[200]},"counts_by_year":[],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2026-03-25T00:00:00"}