{"id":"https://openalex.org/W4406612462","doi":"https://doi.org/10.1109/smc54092.2024.10831176","title":"Latent Vector Autoregressive Modeling with Maximum Predicted Variance for Dynamic Process Monitoring","display_name":"Latent Vector Autoregressive Modeling with Maximum Predicted Variance for Dynamic Process Monitoring","publication_year":2024,"publication_date":"2024-10-06","ids":{"openalex":"https://openalex.org/W4406612462","doi":"https://doi.org/10.1109/smc54092.2024.10831176"},"language":"en","primary_location":{"id":"doi:10.1109/smc54092.2024.10831176","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831176","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5101530219","display_name":"Shumei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shumei Chen","raw_affiliation_strings":["School of Data Science, Lingnan University,Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science, Lingnan University,Hong Kong","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056937548","display_name":"S. Joe Qin","orcid":"https://orcid.org/0000-0001-7631-2535"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S. Joe Qin","raw_affiliation_strings":["School of Data Science, Lingnan University,Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science, Lingnan University,Hong Kong","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1652,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.80091799,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1518","last_page":"1523"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9693999886512756,"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"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9693999886512756,"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-model","display_name":"Autoregressive model","score":0.8330546617507935},{"id":"https://openalex.org/keywords/star-model","display_name":"STAR model","score":0.5839718580245972},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5806518793106079},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5459831953048706},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5353624820709229},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.38901305198669434},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3566109836101532},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.3281967043876648},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27251583337783813},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25512611865997314},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.23155957460403442}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.8330546617507935},{"id":"https://openalex.org/C194657046","wikidata":"https://www.wikidata.org/wiki/Q7394685","display_name":"STAR model","level":4,"score":0.5839718580245972},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5806518793106079},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5459831953048706},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5353624820709229},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.38901305198669434},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3566109836101532},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.3281967043876648},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27251583337783813},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25512611865997314},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.23155957460403442},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"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/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc54092.2024.10831176","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831176","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.6600000262260437}],"awards":[{"id":"https://openalex.org/G2407125372","display_name":null,"funder_award_id":"U20A20189","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1654016072","https://openalex.org/W1978403823","https://openalex.org/W1988581389","https://openalex.org/W1997713206","https://openalex.org/W2038395331","https://openalex.org/W2057283314","https://openalex.org/W2083682440","https://openalex.org/W2135086139","https://openalex.org/W2140327685","https://openalex.org/W2156141384","https://openalex.org/W2169347809","https://openalex.org/W2342028629","https://openalex.org/W2626923521","https://openalex.org/W2765130047","https://openalex.org/W2788023040","https://openalex.org/W2963724581","https://openalex.org/W2964242306","https://openalex.org/W3000195460","https://openalex.org/W3010705831","https://openalex.org/W3040514440","https://openalex.org/W3092838682","https://openalex.org/W3137756630","https://openalex.org/W3171633131","https://openalex.org/W4220772834","https://openalex.org/W4249625715","https://openalex.org/W4250589301","https://openalex.org/W4285246487"],"related_works":["https://openalex.org/W2439807930","https://openalex.org/W2009692134","https://openalex.org/W1972271943","https://openalex.org/W2019155478","https://openalex.org/W2024529895","https://openalex.org/W2168175994","https://openalex.org/W1902630399","https://openalex.org/W2120434453","https://openalex.org/W3120578569","https://openalex.org/W1487412319"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"define":[4],"reduced":[5],"dimensional":[6],"predictors":[7],"in":[8,12,57,87],"latent":[9,26,45],"dynamic":[10,49],"systems":[11],"contrast":[13],"to":[14,35,83,110,115],"the":[15,21,24,37,58,62,104],"traditional":[16],"full-dimensional":[17],"predictor":[18,63],"models.":[19],"Then":[20],"estimation":[22],"of":[23,44],"new":[25,48,93],"vector":[27],"autoregressive":[28],"model":[29],"is":[30,64,81,95],"developed":[31,82],"with":[32,70],"an":[33],"objective":[34],"maximize":[36],"predicted":[38],"variance":[39],"for":[40,55],"a":[41,71,77,98],"given":[42],"number":[43],"variables.":[46],"A":[47],"predictive":[50],"monitoring":[51,79],"index":[52],"that":[53],"accounts":[54],"variations":[56],"prediction":[59],"residual":[60],"and":[61,76,89,103],"developed.":[65],"The":[66,92],"residuals":[67],"are":[68],"modeled":[69],"subsequent":[72],"principal":[73],"component":[74],"analysis":[75],"comprehensive":[78],"method":[80],"detect":[84],"abnormal":[85],"situations":[86],"industrial":[88],"operational":[90],"systems.":[91],"algorithm":[94],"tested":[96],"on":[97],"simple":[99],"closed-loop":[100],"control":[101],"system":[102],"revamped":[105],"Tennessee":[106],"Eastman":[107],"simulated":[108],"process":[109],"show":[111],"its":[112],"effectiveness":[113],"compared":[114],"other":[116],"state-of-the-art":[117],"methods.":[118]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}