{"id":"https://openalex.org/W2513261682","doi":"https://doi.org/10.1051/ro/2016059","title":"A Hybrid ARIMA-ANN approach for optimum estimation and forecasting of gasoline consumption","display_name":"A Hybrid ARIMA-ANN approach for optimum estimation and forecasting of gasoline consumption","publication_year":2016,"publication_date":"2016-09-02","ids":{"openalex":"https://openalex.org/W2513261682","doi":"https://doi.org/10.1051/ro/2016059","mag":"2513261682"},"language":"en","primary_location":{"id":"doi:10.1051/ro/2016059","is_oa":false,"landing_page_url":"https://doi.org/10.1051/ro/2016059","pdf_url":null,"source":{"id":"https://openalex.org/S20631277","display_name":"RAIRO - Operations Research","issn_l":"0399-0559","issn":["0399-0559","1290-3868","2804-7303"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319748","host_organization_name":"EDP Sciences","host_organization_lineage":["https://openalex.org/P4310319748"],"host_organization_lineage_names":["EDP Sciences"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"RAIRO - Operations Research","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.numdam.org/item/10.1051/ro/2016059.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082101979","display_name":"Reza Babazadeh","orcid":"https://orcid.org/0000-0001-8070-6473"},"institutions":[{"id":"https://openalex.org/I38476204","display_name":"Urmia University","ror":"https://ror.org/032fk0x53","country_code":"IR","type":"education","lineage":["https://openalex.org/I38476204"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Reza Babazadeh","raw_affiliation_strings":["Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran","institution_ids":["https://openalex.org/I38476204"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5082101979"],"corresponding_institution_ids":["https://openalex.org/I38476204"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.0787977,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"51","issue":"3","first_page":"719","last_page":"728"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9926999807357788,"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":0.9926999807357788,"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":0.986299991607666,"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":0.9837999939918518,"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-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.9335657358169556},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.6502454280853271},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6429224014282227},{"id":"https://openalex.org/keywords/gasoline","display_name":"Gasoline","score":0.5467631816864014},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5016603469848633},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4745263159275055},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4376920461654663},{"id":"https://openalex.org/keywords/moving-average","display_name":"Moving average","score":0.43558475375175476},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4062163531780243},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.33220040798187256},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.30172163248062134},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.29902184009552},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2635840177536011},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21593210101127625},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18889623880386353}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.9335657358169556},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.6502454280853271},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6429224014282227},{"id":"https://openalex.org/C103697071","wikidata":"https://www.wikidata.org/wiki/Q39558","display_name":"Gasoline","level":2,"score":0.5467631816864014},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5016603469848633},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4745263159275055},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4376920461654663},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.43558475375175476},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4062163531780243},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33220040798187256},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.30172163248062134},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.29902184009552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2635840177536011},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21593210101127625},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18889623880386353},{"id":"https://openalex.org/C548081761","wikidata":"https://www.wikidata.org/wiki/Q180388","display_name":"Waste management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1051/ro/2016059","is_oa":false,"landing_page_url":"https://doi.org/10.1051/ro/2016059","pdf_url":null,"source":{"id":"https://openalex.org/S20631277","display_name":"RAIRO - Operations Research","issn_l":"0399-0559","issn":["0399-0559","1290-3868","2804-7303"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319748","host_organization_name":"EDP Sciences","host_organization_lineage":["https://openalex.org/P4310319748"],"host_organization_lineage_names":["EDP Sciences"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"RAIRO - Operations Research","raw_type":"journal-article"},{"id":"pmh:oai:numdam.org:RO_2017__51_3_719_0","is_oa":true,"landing_page_url":"http://www.numdam.org/articles/10.1051/ro/2016059/","pdf_url":"https://www.numdam.org/item/10.1051/ro/2016059.pdf","source":{"id":"https://openalex.org/S4306402264","display_name":"French digital mathematics library (Numdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I70900168","host_organization_name":"Universit\u00e9 Savoie Mont Blanc","host_organization_lineage":["https://openalex.org/I70900168"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:numdam.org:RO_2017__51_3_719_0","is_oa":true,"landing_page_url":"http://www.numdam.org/articles/10.1051/ro/2016059/","pdf_url":"https://www.numdam.org/item/10.1051/ro/2016059.pdf","source":{"id":"https://openalex.org/S4306402264","display_name":"French digital mathematics library (Numdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I70900168","host_organization_name":"Universit\u00e9 Savoie Mont Blanc","host_organization_lineage":["https://openalex.org/I70900168"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8700000047683716}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320322243","display_name":"Iran National Science Foundation","ror":"https://ror.org/03sr1ma14"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2513261682.pdf","grobid_xml":"https://content.openalex.org/works/W2513261682.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W16076601","https://openalex.org/W1549523743","https://openalex.org/W1561830225","https://openalex.org/W1969681270","https://openalex.org/W1979817014","https://openalex.org/W2010721786","https://openalex.org/W2017933454","https://openalex.org/W2059804518","https://openalex.org/W2110693287","https://openalex.org/W2117014758","https://openalex.org/W2123513648","https://openalex.org/W2146848957","https://openalex.org/W2149391626","https://openalex.org/W2313795686","https://openalex.org/W2313953460","https://openalex.org/W3121926921","https://openalex.org/W3146166473","https://openalex.org/W3207342693"],"related_works":["https://openalex.org/W3139222185","https://openalex.org/W2115811963","https://openalex.org/W2953369890","https://openalex.org/W4379469318","https://openalex.org/W3190289737","https://openalex.org/W4309504760","https://openalex.org/W1767322088","https://openalex.org/W2989181651","https://openalex.org/W2781794331","https://openalex.org/W3199240944"],"abstract_inverted_index":{"Accurate":[0],"estimation":[1,38],"and":[2,10,29,39,53,64,86,149],"forecasting":[3,40,168],"of":[4,41,88,127,139,154,160],"gasoline":[5,42,108,117,169],"is":[6],"vital":[7],"for":[8,36,98],"policy":[9],"decision-making":[11],"process":[12],"in":[13,102,116,119,166],"energy":[14],"sector.":[15],"This":[16],"paper":[17,156],"presents":[18],"a":[19],"hybrid":[20,46,91,134,163],"data-driven":[21],"model":[22,142],"based":[23],"on":[24],"Artificial":[25],"Neural":[26],"Network":[27],"(ANN)":[28],"autoregressive":[30],"integrated":[31],"moving":[32],"average":[33],"(ARIMA)":[34],"approach":[35,48,72,165],"optimum":[37],"consumption.":[43,170],"The":[44,61,137,152],"proposed":[45,90,133,141,162],"ARIMA-ANN":[47,135,164],"considers":[49],"six":[50],"lagged":[51],"variables":[52],"one":[54],"forecasted":[55],"values":[56],"provided":[57],"by":[58,130],"ARIMA":[59,150],"process.":[60,151],"ANN":[62],"trains":[63],"tests":[65],"data":[66,95,115],"with":[67],"Multi":[68],"Layer":[69],"Perceptron":[70],"(MLP)":[71],"which":[73],"has":[74,110],"the":[75,84,89,121,132,140,158,161],"lowest":[76],"Mean":[77],"Absolute":[78],"Percentage":[79],"Error":[80],"(MAPE).":[81],"To":[82],"show":[83,124],"applicability":[85],"superiority":[87],"approach,":[92],"daily":[93],"available":[94],"were":[96],"collected":[97],"7":[99],"years":[100],"(2005\u20132011)":[101],"Iran.":[103],"Although":[104],"eliminating":[105],"subside":[106],"from":[107],"price":[109],"led":[111],"to":[112,146],"appearing":[113],"noisy":[114],"consumption":[118],"Iran":[120],"acquired":[122],"results":[123,138],"high":[125],"accuracy":[126],"about":[128],"9427%":[129],"using":[131],"method.":[136],"are":[143],"compared":[144],"respect":[145],"regression\u2019s":[147],"models":[148],"outcome":[153],"this":[155],"justifies":[157],"capability":[159],"accurate":[167]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-07T06:14:10.401900","created_date":"2025-10-10T00:00:00"}