{"id":"https://openalex.org/W2799373068","doi":"https://doi.org/10.1080/03610918.2018.1458130","title":"Bayesian identification of double seasonal autoregressive time series models","display_name":"Bayesian identification of double seasonal autoregressive time series models","publication_year":2018,"publication_date":"2018-05-08","ids":{"openalex":"https://openalex.org/W2799373068","doi":"https://doi.org/10.1080/03610918.2018.1458130","mag":"2799373068"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2018.1458130","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2018.1458130","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"type":"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/A5045330318","display_name":"Ayman A. Amin","orcid":"https://orcid.org/0000-0003-1560-7609"},"institutions":[{"id":"https://openalex.org/I63601056","display_name":"Menoufia University","ror":"https://ror.org/05sjrb944","country_code":"EG","type":"education","lineage":["https://openalex.org/I63601056"]}],"countries":["EG"],"is_corresponding":true,"raw_author_name":"Ayman A. Amin","raw_affiliation_strings":["Department of Statistics, Mathematics, and Insurance, Faculty of Commerce, Menoufia University, Menoufia, Egypt"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Mathematics, and Insurance, Faculty of Commerce, Menoufia University, Menoufia, Egypt","institution_ids":["https://openalex.org/I63601056"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5045330318"],"corresponding_institution_ids":["https://openalex.org/I63601056"],"apc_list":null,"apc_paid":null,"fwci":0.3863,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.61664018,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"48","issue":"8","first_page":"2501","last_page":"2511"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9972000122070312,"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":0.9972000122070312,"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":0.991100013256073,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9717000126838684,"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/prior-probability","display_name":"Prior probability","score":0.7919629812240601},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.7738302946090698},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6831138134002686},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.6192257404327393},{"id":"https://openalex.org/keywords/conjugate-prior","display_name":"Conjugate prior","score":0.5306063294410706},{"id":"https://openalex.org/keywords/star-model","display_name":"STAR model","score":0.53041011095047},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.5205149054527283},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.5016844272613525},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.4975028336048126},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4924691617488861},{"id":"https://openalex.org/keywords/autoregressive\u2013moving-average-model","display_name":"Autoregressive\u2013moving-average model","score":0.46048909425735474},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4301232695579529},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4207746088504791},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.37677091360092163},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.36805540323257446},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3517265319824219},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3209661841392517},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2552593946456909},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2179446816444397},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.21139663457870483}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7919629812240601},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7738302946090698},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6831138134002686},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.6192257404327393},{"id":"https://openalex.org/C26004113","wikidata":"https://www.wikidata.org/wiki/Q3711784","display_name":"Conjugate prior","level":4,"score":0.5306063294410706},{"id":"https://openalex.org/C194657046","wikidata":"https://www.wikidata.org/wiki/Q7394685","display_name":"STAR model","level":4,"score":0.53041011095047},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5205149054527283},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.5016844272613525},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.4975028336048126},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4924691617488861},{"id":"https://openalex.org/C74883015","wikidata":"https://www.wikidata.org/wiki/Q290467","display_name":"Autoregressive\u2013moving-average model","level":3,"score":0.46048909425735474},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4301232695579529},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4207746088504791},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.37677091360092163},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.36805540323257446},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3517265319824219},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3209661841392517},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2552593946456909},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2179446816444397},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.21139663457870483},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2018.1458130","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2018.1458130","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W625867593","https://openalex.org/W1560354765","https://openalex.org/W1968536546","https://openalex.org/W1969793315","https://openalex.org/W1985815014","https://openalex.org/W1996102080","https://openalex.org/W2003936022","https://openalex.org/W2018559635","https://openalex.org/W2025639884","https://openalex.org/W2043988807","https://openalex.org/W2081503608","https://openalex.org/W2092335170","https://openalex.org/W2109316012","https://openalex.org/W2119996119","https://openalex.org/W2142624110","https://openalex.org/W2145732764","https://openalex.org/W2152933101","https://openalex.org/W2292235900","https://openalex.org/W2752607453","https://openalex.org/W2763636370","https://openalex.org/W2767443199","https://openalex.org/W2772768362","https://openalex.org/W2791087833","https://openalex.org/W2798058877","https://openalex.org/W2892077551","https://openalex.org/W2978715007","https://openalex.org/W3015394372","https://openalex.org/W4229539396","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2041819240","https://openalex.org/W2104468875","https://openalex.org/W199725827","https://openalex.org/W4248896451","https://openalex.org/W1484537345","https://openalex.org/W4249363612","https://openalex.org/W2349453449","https://openalex.org/W1768043922","https://openalex.org/W2228548008","https://openalex.org/W348668005"],"abstract_inverted_index":{"Seasonal":[0],"autoregressive":[1,30],"(SAR)":[2],"models":[3],"have":[4,23],"been":[5],"modified":[6],"and":[7,71,79,108,140],"extended":[8],"to":[9,48,57,145],"model":[10,66,84,95,114],"high":[11],"frequency":[12],"time":[13,127],"series":[14,128],"characterized":[15],"by":[16],"exhibiting":[17],"double":[18,28],"seasonal":[19,29],"patterns.":[20],"Some":[21],"researchers":[22],"introduced":[24],"Bayesian":[25,40,55,135],"inference":[26],"for":[27,125],"(DSAR)":[31],"models;":[32],"however,":[33],"none":[34],"has":[35],"tackled":[36],"the":[37,59,65,83,88,94,101,109,121,126,133],"problem":[38],"of":[39,42,61,93,112,150],"identification":[41],"DSAR":[43,62,113],"models.":[44,63],"Therefore,":[45],"in":[46,97],"order":[47,60,96,111],"fill":[49],"this":[50],"gap,":[51],"we":[52,86,141],"present":[53],"a":[54,98,118],"methodology":[56,136],"identify":[58],"Assuming":[64],"errors":[67],"are":[68],"normally":[69],"distributed":[70],"using":[72,137],"three":[73],"priors,":[74,81],"i.e.":[75],"natural":[76],"conjugate,":[77],"g,":[78],"Jeffreys\u2019":[80],"on":[82],"parameters,":[85],"derive":[87],"joint":[89],"posterior":[90,102,123],"mass":[91,103],"function":[92,104],"closed-form.":[99],"Accordingly,":[100],"can":[105],"be":[106],"investigated":[107],"best":[110],"is":[115],"chosen":[116],"as":[117],"value":[119],"with":[120],"highest":[122],"probability":[124],"being":[129],"analyzed.":[130],"We":[131],"evaluate":[132],"proposed":[134],"simulation":[138],"study,":[139],"then":[142],"apply":[143],"it":[144],"real-world":[146],"hourly":[147],"internet":[148],"amount":[149],"traffic":[151],"dataset.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}