{"id":"https://openalex.org/W2582992257","doi":"https://doi.org/10.1155/2017/7436948","title":"Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network","display_name":"Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2582992257","doi":"https://doi.org/10.1155/2017/7436948","mag":"2582992257","pmid":"https://pubmed.ncbi.nlm.nih.gov/28246527"},"language":"en","primary_location":{"id":"doi:10.1155/2017/7436948","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2017/7436948","pdf_url":"http://downloads.hindawi.com/journals/cin/2017/7436948.pdf","source":{"id":"https://openalex.org/S72372694","display_name":"Computational Intelligence and Neuroscience","issn_l":"1687-5265","issn":["1687-5265","1687-5273"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Intelligence and Neuroscience","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"http://downloads.hindawi.com/journals/cin/2017/7436948.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009404767","display_name":"Ying Yu","orcid":"https://orcid.org/0009-0006-8230-5378"},"institutions":[{"id":"https://openalex.org/I42766147","display_name":"University of Toyama","ror":"https://ror.org/0445phv87","country_code":"JP","type":"education","lineage":["https://openalex.org/I42766147"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ying Yu","raw_affiliation_strings":["Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan","institution_ids":["https://openalex.org/I42766147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006282634","display_name":"Yirui Wang","orcid":"https://orcid.org/0000-0001-5767-3343"},"institutions":[{"id":"https://openalex.org/I42766147","display_name":"University of Toyama","ror":"https://ror.org/0445phv87","country_code":"JP","type":"education","lineage":["https://openalex.org/I42766147"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yirui Wang","raw_affiliation_strings":["Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan","institution_ids":["https://openalex.org/I42766147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010245958","display_name":"Shangce Gao","orcid":"https://orcid.org/0000-0001-5042-3261"},"institutions":[{"id":"https://openalex.org/I42766147","display_name":"University of Toyama","ror":"https://ror.org/0445phv87","country_code":"JP","type":"education","lineage":["https://openalex.org/I42766147"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Shangce Gao","raw_affiliation_strings":["Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan","institution_ids":["https://openalex.org/I42766147"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100704278","display_name":"Zheng Tang","orcid":"https://orcid.org/0000-0002-6543-7444"},"institutions":[{"id":"https://openalex.org/I42766147","display_name":"University of Toyama","ror":"https://ror.org/0445phv87","country_code":"JP","type":"education","lineage":["https://openalex.org/I42766147"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zheng Tang","raw_affiliation_strings":["Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan","institution_ids":["https://openalex.org/I42766147"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5010245958"],"corresponding_institution_ids":["https://openalex.org/I42766147"],"apc_list":{"value":2100,"currency":"USD","value_usd":2100},"apc_paid":{"value":2100,"currency":"USD","value_usd":2100},"fwci":6.4565,"has_fulltext":true,"cited_by_count":41,"citation_normalized_percentile":{"value":0.96490501,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"2017","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9979000091552734,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9979000091552734,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9959999918937683,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9890999794006348,"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/autoregressive-model","display_name":"Autoregressive model","score":0.7307325601577759},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7293989658355713},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6835120916366577},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5803255438804626},{"id":"https://openalex.org/keywords/tourism","display_name":"Tourism","score":0.5150117874145508},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.503527820110321},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4863050878047943},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.46527761220932007},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4602604806423187},{"id":"https://openalex.org/keywords/moving-average-model","display_name":"Moving-average model","score":0.43739235401153564},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.4366825819015503},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.43626293540000916},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.33598077297210693},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.28364068269729614},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2566278278827667},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21232852339744568},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18583941459655762},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.15886560082435608},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0716044008731842}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7307325601577759},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7293989658355713},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6835120916366577},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5803255438804626},{"id":"https://openalex.org/C18918823","wikidata":"https://www.wikidata.org/wiki/Q49389","display_name":"Tourism","level":2,"score":0.5150117874145508},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.503527820110321},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4863050878047943},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.46527761220932007},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4602604806423187},{"id":"https://openalex.org/C155380588","wikidata":"https://www.wikidata.org/wiki/Q1088984","display_name":"Moving-average model","level":4,"score":0.43739235401153564},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.4366825819015503},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.43626293540000916},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.33598077297210693},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28364068269729614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2566278278827667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21232852339744568},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18583941459655762},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15886560082435608},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0716044008731842},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":4,"locations":[{"id":"doi:10.1155/2017/7436948","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2017/7436948","pdf_url":"http://downloads.hindawi.com/journals/cin/2017/7436948.pdf","source":{"id":"https://openalex.org/S72372694","display_name":"Computational Intelligence and Neuroscience","issn_l":"1687-5265","issn":["1687-5265","1687-5273"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Intelligence and Neuroscience","raw_type":"journal-article"},{"id":"pmid:28246527","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/28246527","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational intelligence and neuroscience","raw_type":null},{"id":"pmh:oai:doaj.org/article:991d80227f314385a31fb457ac48f401","is_oa":true,"landing_page_url":"https://doaj.org/article/991d80227f314385a31fb457ac48f401","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":"Computational Intelligence and Neuroscience, Vol 2017 (2017)","raw_type":"article"},{"id":"pmh:oai:europepmc.org:4180466","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5299217","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"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":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1155/2017/7436948","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2017/7436948","pdf_url":"http://downloads.hindawi.com/journals/cin/2017/7436948.pdf","source":{"id":"https://openalex.org/S72372694","display_name":"Computational Intelligence and Neuroscience","issn_l":"1687-5265","issn":["1687-5265","1687-5273"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Intelligence and Neuroscience","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6299999952316284,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G1069223013","display_name":null,"funder_award_id":"JSPS KAKENHI","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G3459562248","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G4636223006","display_name":null,"funder_award_id":"JSPS KAK","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7513472892","display_name":null,"funder_award_id":"15K00332","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2582992257.pdf","grobid_xml":"https://content.openalex.org/works/W2582992257.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W11904183","https://openalex.org/W1571936616","https://openalex.org/W1586335931","https://openalex.org/W1971259144","https://openalex.org/W1986528915","https://openalex.org/W1992347171","https://openalex.org/W1995341919","https://openalex.org/W2006746888","https://openalex.org/W2013311305","https://openalex.org/W2014260475","https://openalex.org/W2015122449","https://openalex.org/W2018466646","https://openalex.org/W2028702910","https://openalex.org/W2030888282","https://openalex.org/W2045581228","https://openalex.org/W2067284569","https://openalex.org/W2081486763","https://openalex.org/W2086691529","https://openalex.org/W2102492019","https://openalex.org/W2104044232","https://openalex.org/W2107033927","https://openalex.org/W2119608925","https://openalex.org/W2126610602","https://openalex.org/W2129214104","https://openalex.org/W2150705443","https://openalex.org/W2236727783","https://openalex.org/W2291445757","https://openalex.org/W2400522988","https://openalex.org/W2500086770","https://openalex.org/W2509512661","https://openalex.org/W2764408916","https://openalex.org/W2764769393","https://openalex.org/W2795454296","https://openalex.org/W2798058877","https://openalex.org/W3126014112"],"related_works":["https://openalex.org/W4400247370","https://openalex.org/W3139222185","https://openalex.org/W2953369890","https://openalex.org/W2115811963","https://openalex.org/W4388101383","https://openalex.org/W4379469318","https://openalex.org/W1767322088","https://openalex.org/W2989181651","https://openalex.org/W74935964","https://openalex.org/W2781794331"],"abstract_inverted_index":{"With":[0],"the":[1,32,50,57,69,76,80,91,97,110,113,119,126,131,137,147],"impact":[2],"of":[3,112,146,154],"global":[4],"internationalization,":[5],"tourism":[6,51],"economy":[7],"has":[8],"also":[9,117,134],"been":[10],"a":[11,87],"rapid":[12],"development.":[13],"The":[14],"increasing":[15],"interest":[16],"aroused":[17],"by":[18,79],"more":[19],"advanced":[20],"forecasting":[21,27],"methods":[22],"leads":[23],"us":[24],"to":[25,48,67,108],"innovate":[26],"methods.":[28],"In":[29,106],"this":[30,95],"paper,":[31,96],"seasonal":[33,58],"trend":[34,59,72],"autoregressive":[35,60],"integrated":[36,61],"moving":[37,62],"averages":[38,63],"with":[39],"dendritic":[40,81],"neural":[41,82],"network":[42,83],"model":[43,64,84,99,139],"(SA-D":[44],"model)":[45,66],"is":[46],"proposed":[47],"perform":[49],"demand":[52],"forecasting.":[53],"First,":[54],"we":[55,116],"use":[56,118],"(SARIMA":[65],"exclude":[68],"long-term":[70],"linear":[71],"and":[73,85,129,156],"then":[74],"train":[75],"residual":[77],"data":[78,120],"make":[86],"short-term":[88],"prediction.":[89],"As":[90],"result":[92],"showed":[93],"in":[94,125,144],"SA-D":[98,114,138],"can":[100],"achieve":[101],"considerably":[102],"better":[103],"predictive":[104,142],"performances.":[105],"order":[107],"demonstrate":[109],"effectiveness":[111],"model,":[115],"that":[121,136],"other":[122,127],"authors":[123],"used":[124],"models":[128],"compare":[130],"results.":[132],"It":[133],"proved":[135],"achieved":[140],"good":[141],"performances":[143],"terms":[145],"normalized":[148],"mean":[149],"square":[150],"error,":[151,155],"absolute":[152],"percentage":[153],"correlation":[157],"coefficient.":[158]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}