{"id":"https://openalex.org/W4380433143","doi":"https://doi.org/10.1145/3588951","title":"AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting","display_name":"AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting","publication_year":2023,"publication_date":"2023-05-26","ids":{"openalex":"https://openalex.org/W4380433143","doi":"https://doi.org/10.1145/3588951"},"language":"en","primary_location":{"id":"doi:10.1145/3588951","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3588951","pdf_url":null,"source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","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/A5101735806","display_name":"Xinle Wu","orcid":"https://orcid.org/0000-0002-2892-7153"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Xinle Wu","raw_affiliation_strings":["Aalborg University, Aalborg, Denmark"],"affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101753289","display_name":"Dalin Zhang","orcid":"https://orcid.org/0000-0002-5869-6544"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Dalin Zhang","raw_affiliation_strings":["Aalborg University, Aalborg, Denmark"],"affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101137578","display_name":"Miao Zhang","orcid":"https://orcid.org/0000-0002-1262-4174"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miao Zhang","raw_affiliation_strings":["Harbin lnstitute ofTechnology & Aalborg University, Shenzhen, Aalborg, China"],"affiliations":[{"raw_affiliation_string":"Harbin lnstitute ofTechnology & Aalborg University, Shenzhen, Aalborg, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084021933","display_name":"Chenjuan Guo","orcid":"https://orcid.org/0000-0002-4516-4637"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenjuan Guo","raw_affiliation_strings":["East China Normal University & Aalborg University, Shanghai, Aalborg, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University & Aalborg University, Shanghai, Aalborg, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072309548","display_name":"Bin Yang","orcid":"https://orcid.org/0000-0002-1658-1079"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Yang","raw_affiliation_strings":["East China Normal University & Aalborg University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University & Aalborg University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029380368","display_name":"Christian S. Jensen","orcid":"https://orcid.org/0000-0002-9697-7670"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Christian S. Jensen","raw_affiliation_strings":["Aalborg University, Aalborg, Denmark"],"affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Denmark","institution_ids":["https://openalex.org/I891191580"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101735806"],"corresponding_institution_ids":["https://openalex.org/I891191580"],"apc_list":null,"apc_paid":null,"fwci":6.3064,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.97507114,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"1","issue":"1","first_page":"1","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9970999956130981,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9861999750137329,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.9100362062454224},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7984210848808289},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7100397944450378},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6215377449989319},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6032445430755615},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5649427175521851},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5628437399864197},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.5570133924484253},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4934057295322418},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.419005423784256},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33300483226776123},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.08898657560348511}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9100362062454224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7984210848808289},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7100397944450378},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6215377449989319},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6032445430755615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5649427175521851},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5628437399864197},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.5570133924484253},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4934057295322418},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.419005423784256},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33300483226776123},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.08898657560348511},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3588951","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3588951","pdf_url":null,"source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:publications/70c2a283-eb6a-47e2-8101-5b7db0594ee4","is_oa":false,"landing_page_url":"https://vbn.aau.dk/da/publications/70c2a283-eb6a-47e2-8101-5b7db0594ee4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Wu , X , Zhang , D , Zhang , M , Guo , C , Yang , B & Jensen , C S 2023 , ' AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting. ' , Proceedings of the ACM on Management of Data , vol. 1 , no. 1 , pp. 97:1-97:26 . https://doi.org/10.1145/3588951","raw_type":"contributionToPeriodical"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.46000000834465027}],"awards":[{"id":"https://openalex.org/G8338820310","display_name":null,"funder_award_id":"34328 40567","funder_id":"https://openalex.org/F4320310490","funder_display_name":"Villum Fonden"}],"funders":[{"id":"https://openalex.org/F4320310490","display_name":"Villum Fonden","ror":"https://ror.org/007ww2d15"},{"id":"https://openalex.org/F4320313796","display_name":"Innovationsfonden","ror":"https://ror.org/00daj4111"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2604847698","https://openalex.org/W2614475470","https://openalex.org/W2756203131","https://openalex.org/W2890096158","https://openalex.org/W2948858042","https://openalex.org/W2965341826","https://openalex.org/W2987715998","https://openalex.org/W2996847713","https://openalex.org/W2998239226","https://openalex.org/W3011820231","https://openalex.org/W3012562343","https://openalex.org/W3037671731","https://openalex.org/W3038981236","https://openalex.org/W3093761440","https://openalex.org/W3094231942","https://openalex.org/W3096533519","https://openalex.org/W3103427490","https://openalex.org/W3103720336","https://openalex.org/W3156351347","https://openalex.org/W3173166478","https://openalex.org/W3173807327","https://openalex.org/W3175924508","https://openalex.org/W3177318507","https://openalex.org/W3185616770","https://openalex.org/W3194119111","https://openalex.org/W3210924156","https://openalex.org/W4206626673","https://openalex.org/W4225862894","https://openalex.org/W4285184111","https://openalex.org/W4287715829","https://openalex.org/W4289533840","https://openalex.org/W4289533938","https://openalex.org/W6600075759","https://openalex.org/W6600175266","https://openalex.org/W6672722415","https://openalex.org/W6786080308"],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4281646320","https://openalex.org/W4205712847","https://openalex.org/W3169687406","https://openalex.org/W1974336862","https://openalex.org/W4388119537","https://openalex.org/W3014750173","https://openalex.org/W3114025147","https://openalex.org/W4287818966","https://openalex.org/W3192751261"],"abstract_inverted_index":{"Sensors":[0],"in":[1,87],"cyber-physical":[2],"systems":[3],"often":[4],"capture":[5],"interconnected":[6],"processes":[7],"and":[8,37,90,101,131,151,192],"thus":[9],"emit":[10],"correlated":[11],"time":[12,35,42],"series":[13,36],"(CTS),":[14],"the":[15,31,38,60,156,165],"forecasting":[16,27,71,123],"of":[17,34,62,186],"which":[18],"enables":[19,70],"important":[20],"applications.":[21],"The":[22],"key":[23],"to":[24,29,94,104,118,146,159,204],"successful":[25],"CTS":[26,57,84,122,196],"is":[28,68,184],"uncover":[30],"temporal":[32],"dynamics":[33],"spatial":[39],"correlations":[40],"among":[41],"series.":[43],"Deep":[44],"learning-based":[45],"solutions":[46,85],"exhibit":[47],"impressive":[48],"performance":[49,188],"at":[50],"discerning":[51],"these":[52,109],"aspects.":[53],"In":[54,198],"particular,":[55],"automated":[56,83],"forecasting,":[58],"where":[59],"design":[61],"an":[63,141,161],"optimal":[64,96],"deep":[65],"learning":[66],"architecture":[67,130],"automated,":[69],"accuracy":[72],"that":[73,175],"surpasses":[74],"what":[75],"has":[76],"been":[77],"achieved":[78],"by":[79],"manual":[80,180],"approaches.":[81],"However,":[82],"remain":[86],"their":[88],"infancy":[89],"are":[91],"only":[92,178],"able":[93],"find":[95],"architectures":[97],"for":[98],"predefined":[99],"hyperparameters":[100,133],"scale":[102],"poorly":[103],"large-scale":[105],"CTS.":[106,206],"To":[107],"overcome":[108],"limitations,":[110],"we":[111,126,152],"propose":[112],"AutoCTS+,":[113],"a":[114,135],"joint,":[115],"scalable":[116],"framework,":[117],"automatically":[119,194],"devise":[120],"effective":[121],"models.":[124,197],"Specifically,":[125],"encode":[127],"each":[128],"candidate":[129],"accompanying":[132],"into":[134],"joint":[136],"graph":[137],"representation.":[138],"We":[139],"introduce":[140],"efficient":[142],"Architecture-Hyperparameter":[143],"Comparator":[144],"(AHC)":[145],"rank":[147],"all":[148],"architecture-hyperparameter":[149,162],"pairs,":[150],"then":[153],"further":[154],"evaluate":[155],"top-ranked":[157],"pairs":[158],"select":[160],"pair":[163],"as":[164],"final":[166],"model.":[167],"Extensive":[168],"experiments":[169],"on":[170],"six":[171],"benchmark":[172],"datasets":[173],"demonstrate":[174],"AutoCTS+":[176],"not":[177],"eliminates":[179],"efforts":[181],"but":[182],"also":[183],"capable":[185],"better":[187],"than":[189],"manually":[190],"designed":[191,195],"existing":[193],"addition,":[199],"it":[200],"shows":[201],"excellent":[202],"scalability":[203],"large":[205]},"counts_by_year":[{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}