{"id":"https://openalex.org/W4389314674","doi":"https://doi.org/10.1145/3627377.3627381","title":"A Predictive Study of ARIMA Model Based on Multi-Bayesian Estimation Method Fused Data on Building Materials Environment","display_name":"A Predictive Study of ARIMA Model Based on Multi-Bayesian Estimation Method Fused Data on Building Materials Environment","publication_year":2023,"publication_date":"2023-09-22","ids":{"openalex":"https://openalex.org/W4389314674","doi":"https://doi.org/10.1145/3627377.3627381"},"language":"en","primary_location":{"id":"doi:10.1145/3627377.3627381","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627377.3627381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Big Data Technologies","raw_type":"proceedings-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/A5055769459","display_name":"Yan Xu","orcid":"https://orcid.org/0009-0008-6817-4740"},"institutions":[{"id":"https://openalex.org/I59649739","display_name":"Jiangxi University of Finance and Economics","ror":"https://ror.org/03efmyj29","country_code":"CN","type":"education","lineage":["https://openalex.org/I59649739"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yan Xu","raw_affiliation_strings":["School of Software &amp; Internet of Things Engineering, Jiangxi University of Finance and Economics, China"],"raw_orcid":"https://orcid.org/0009-0008-6817-4740","affiliations":[{"raw_affiliation_string":"School of Software &amp; Internet of Things Engineering, Jiangxi University of Finance and Economics, China","institution_ids":["https://openalex.org/I59649739"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101407140","display_name":"Gaoyun Wu","orcid":"https://orcid.org/0009-0008-5462-240X"},"institutions":[{"id":"https://openalex.org/I59649739","display_name":"Jiangxi University of Finance and Economics","ror":"https://ror.org/03efmyj29","country_code":"CN","type":"education","lineage":["https://openalex.org/I59649739"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaoyun Wu","raw_affiliation_strings":["School of Software &amp; Internet of Things Engineering, Jiangxi University of Finance and Economics, China"],"raw_orcid":"https://orcid.org/0009-0008-5462-240X","affiliations":[{"raw_affiliation_string":"School of Software &amp; Internet of Things Engineering, Jiangxi University of Finance and Economics, China","institution_ids":["https://openalex.org/I59649739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052028589","display_name":"William Wei Song","orcid":"https://orcid.org/0000-0003-3681-8173"},"institutions":[{"id":"https://openalex.org/I122274158","display_name":"Dalarna University","ror":"https://ror.org/000hdh770","country_code":"SE","type":"education","lineage":["https://openalex.org/I122274158"]},{"id":"https://openalex.org/I59649739","display_name":"Jiangxi University of Finance and Economics","ror":"https://ror.org/03efmyj29","country_code":"CN","type":"education","lineage":["https://openalex.org/I59649739"]}],"countries":["CN","SE"],"is_corresponding":false,"raw_author_name":"Wei Song","raw_affiliation_strings":["School of Information Management, Dalarna University, Sweden and \rSchool of Software &amp; Internet of Things Engineering, Jiangxi University of Finance and Economics, China"],"raw_orcid":"https://orcid.org/0000-0003-3681-8173","affiliations":[{"raw_affiliation_string":"School of Information Management, Dalarna University, Sweden and \rSchool of Software &amp; Internet of Things Engineering, Jiangxi University of Finance and Economics, China","institution_ids":["https://openalex.org/I59649739","https://openalex.org/I122274158"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5055769459"],"corresponding_institution_ids":["https://openalex.org/I59649739"],"apc_list":null,"apc_paid":null,"fwci":0.1073,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42660801,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"22","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9539999961853027,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9539999961853027,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10320","display_name":"Neural Networks and Applications","score":0.9484999775886536,"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"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9387999773025513,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.6749133467674255},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.6536936163902283},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.648784339427948},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6459131240844727},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.64008629322052},{"id":"https://openalex.org/keywords/data-redundancy","display_name":"Data redundancy","score":0.6362608671188354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6293635964393616},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6281209588050842},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5159812569618225},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5065056085586548},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4321768879890442},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.384670615196228},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3107554614543915},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29778575897216797},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21038490533828735}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.6749133467674255},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6536936163902283},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.648784339427948},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6459131240844727},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.64008629322052},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.6362608671188354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6293635964393616},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6281209588050842},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5159812569618225},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5065056085586548},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4321768879890442},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.384670615196228},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3107554614543915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29778575897216797},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21038490533828735},{"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627377.3627381","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627377.3627381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Big Data Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1988316045","https://openalex.org/W2033157981","https://openalex.org/W2038420319","https://openalex.org/W2924944420","https://openalex.org/W3015228848","https://openalex.org/W3167788734","https://openalex.org/W4319599691","https://openalex.org/W4385445785"],"related_works":["https://openalex.org/W2354322770","https://openalex.org/W3000097931","https://openalex.org/W3135881084","https://openalex.org/W2154495931","https://openalex.org/W2056959780","https://openalex.org/W2010131506","https://openalex.org/W2027710607","https://openalex.org/W4200005349","https://openalex.org/W4281929741","https://openalex.org/W2132308729"],"abstract_inverted_index":{"Due":[0],"to":[1,23,48,66],"the":[2,13,25,50,62,72],"uncertainty":[3,69],"and":[4,41,55,70],"inconsistency":[5],"of":[6,28,53,64,74],"measurement":[7,26],"data":[8,18,27,65],"from":[9],"multiple":[10,29],"sensors":[11],"in":[12],"same":[14],"space,":[15],"a":[16,33],"multi-sensor":[17,39],"fusion":[19],"algorithm":[20],"is":[21],"used":[22],"fuse":[24],"nodes.":[30],"We":[31],"propose":[32],"multi-Bayesian":[34],"estimation":[35,44],"method":[36],"for":[37],"fusing":[38],"data,":[40],"combine":[42],"Bayesian":[43],"with":[45],"ARIMA":[46],"model":[47],"predict":[49],"ambient":[51],"temperature":[52],"bamboo":[54],"wood":[56],"building":[57],"materials.":[58],"It":[59],"can":[60],"utilize":[61],"redundancy":[63],"reduce":[67],"this":[68],"improve":[71],"reliability":[73],"subsequent":[75],"predictions.":[76]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
