{"id":"https://openalex.org/W4413374932","doi":"https://doi.org/10.1186/s40537-025-01266-8","title":"Task-aware conditional GAN with multi-objective loss for realistic and efficient industrial time series generation","display_name":"Task-aware conditional GAN with multi-objective loss for realistic and efficient industrial time series generation","publication_year":2025,"publication_date":"2025-08-21","ids":{"openalex":"https://openalex.org/W4413374932","doi":"https://doi.org/10.1186/s40537-025-01266-8"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-025-01266-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01266-8","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01266-8","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01266-8","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111767389","display_name":"K. Lang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kai Lang","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, 100876, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, 100876, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044955741","display_name":"Yonghua Li","orcid":"https://orcid.org/0000-0001-7935-9152"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghua Li","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, 100876, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, 100876, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111767389"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":2.7092,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.91043554,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"12","issue":"1","first_page":null,"last_page":null},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9958000183105469,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9842000007629395,"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/computer-science","display_name":"Computer science","score":0.8296188116073608},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7185912728309631},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6918205618858337},{"id":"https://openalex.org/keywords/computational-science-and-engineering","display_name":"Computational Science and Engineering","score":0.5813943147659302},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3617933988571167},{"id":"https://openalex.org/keywords/computational-science","display_name":"Computational science","score":0.3126125931739807},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.07625308632850647}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8296188116073608},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7185912728309631},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6918205618858337},{"id":"https://openalex.org/C68597687","wikidata":"https://www.wikidata.org/wiki/Q362601","display_name":"Computational Science and Engineering","level":2,"score":0.5813943147659302},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3617933988571167},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.3126125931739807},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.07625308632850647},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"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":2,"locations":[{"id":"doi:10.1186/s40537-025-01266-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01266-8","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01266-8","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6103751b4c1c419cb2ba1667c8b7612e","is_oa":true,"landing_page_url":"https://doaj.org/article/6103751b4c1c419cb2ba1667c8b7612e","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 12, Iss 1, Pp 1-21 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-025-01266-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01266-8","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01266-8","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G535022146","display_name":null,"funder_award_id":"52274026","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":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413374932.pdf","grobid_xml":"https://content.openalex.org/works/W4413374932.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W2766296277","https://openalex.org/W3003265726","https://openalex.org/W3008872739","https://openalex.org/W3015316773","https://openalex.org/W3093676973","https://openalex.org/W3199905512","https://openalex.org/W3204937802","https://openalex.org/W4226179997","https://openalex.org/W4285505395","https://openalex.org/W4309332691","https://openalex.org/W4312384107","https://openalex.org/W4312386872","https://openalex.org/W4313007581","https://openalex.org/W4316021877","https://openalex.org/W4319338551","https://openalex.org/W4382047854","https://openalex.org/W4382775434","https://openalex.org/W4387039109","https://openalex.org/W4387885619","https://openalex.org/W4394897005","https://openalex.org/W4398226253","https://openalex.org/W4400877832","https://openalex.org/W4401113482","https://openalex.org/W4402742292","https://openalex.org/W4407461396","https://openalex.org/W4408853537","https://openalex.org/W6735913928","https://openalex.org/W6818723395"],"related_works":["https://openalex.org/W4393232657","https://openalex.org/W4412093237","https://openalex.org/W4410929354","https://openalex.org/W4390638272","https://openalex.org/W1919101720","https://openalex.org/W4408313902","https://openalex.org/W2472237121","https://openalex.org/W4323316863","https://openalex.org/W1985111449","https://openalex.org/W4390822878"],"abstract_inverted_index":{"Industrial":[0],"time-series":[1,185],"data":[2,8,79],"generation":[3,102,125,186],"is":[4],"critical":[5],"for":[6,94,107],"addressing":[7],"scarcity,":[9],"improving":[10],"model":[11,122,161],"robustness,":[12],"and":[13,34,60,70,84,97,127,141,148,152,177],"enabling":[14],"data-driven":[15],"decision-making":[16],"in":[17,183],"complex":[18,108],"manufacturing":[19],"systems.":[20],"However,":[21],"existing":[22],"generative":[23,49],"models":[24],"often":[25],"suffer":[26],"from":[27],"poor":[28],"temporal":[29,58,109],"fidelity,":[30],"limited":[31],"statistical":[32,54,71],"consistency,":[33],"weak":[35],"adaptability":[36],"to":[37,130,170],"downstream":[38],"tasks.":[39],"To":[40],"address":[41],"these":[42],"challenges,":[43],"we":[44],"propose":[45],"a":[46,61,91,163],"novel":[47],"conditional":[48],"adversarial":[50],"framework":[51,182],"that":[52,76,120],"integrates":[53],"feature":[55],"augmentation,":[56],"multi-scale":[57],"windows,":[59],"composite":[62],"loss":[63,95],"function":[64],"combining":[65],"adversarial,":[66],"L2,":[67],"DTW,":[68],"FID,":[69],"constraints.":[72],"This":[73],"design":[74],"ensures":[75],"the":[77,105,137,142,175],"generated":[78],"preserves":[80],"both":[81],"local":[82],"dynamics":[83],"global":[85],"distributional":[86],"properties.":[87],"Our":[88],"method":[89,135],"introduces":[90],"systematic":[92],"strategy":[93],"calibration":[96],"architecture":[98],"tuning,":[99],"which":[100],"enhances":[101],"stability":[103],"without":[104],"need":[106],"signature":[110],"modeling.":[111],"Experimental":[112],"results":[113,173],"on":[114],"three":[115],"real-world":[116,184],"industrial":[117],"datasets":[118],"demonstrate":[119],"our":[121,134,160,180],"achieves":[123,136,162],"superior":[124],"fidelity":[126],"efficiency":[128,178],"compared":[129,169],"state-of-the-art":[131],"baselines.":[132],"Specifically,":[133],"lowest":[138,143],"DTW":[139],"score":[140],"MMD":[144],"value,":[145],"surpassing":[146],"MTSS-GAN":[147],"TCGAN":[149],"by":[150],"17.3%":[151],"21.7%,":[153],"respectively.":[154],"In":[155],"terms":[156],"of":[157,167,179],"training":[158,164],"cost,":[159],"time":[165],"reduction":[166],"11.4%":[168],"MTSS-GAN.":[171],"These":[172],"validate":[174],"effectiveness":[176],"proposed":[181],"scenarios.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}