{"id":"https://openalex.org/W4402351794","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651016","title":"One-Pass Generation of Multivariate Time Series through Conditional Multivariate Modeling","display_name":"One-Pass Generation of Multivariate Time Series through Conditional Multivariate Modeling","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351794","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651016"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651016","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-04713504","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034238046","display_name":"Abdellah Madane","orcid":null},"institutions":[{"id":"https://openalex.org/I195731000","display_name":"Universit\u00e9 de Versailles Saint-Quentin-en-Yvelines","ror":"https://ror.org/03mkjjy25","country_code":"FR","type":"education","lineage":["https://openalex.org/I195731000","https://openalex.org/I277688954"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I4390039268","display_name":"Donn\u00e9es et algorithmes pour une ville intelligente et durable","ror":"https://ror.org/01xta2p78","country_code":"FR","type":"facility","lineage":["https://openalex.org/I195731000","https://openalex.org/I277688954","https://openalex.org/I4390039268"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Abdellah Madane","raw_affiliation_strings":["Paris-Saclay University,DAVID Lab, UVSQ,Versailles,France"],"affiliations":[{"raw_affiliation_string":"Paris-Saclay University,DAVID Lab, UVSQ,Versailles,France","institution_ids":["https://openalex.org/I195731000","https://openalex.org/I277688954","https://openalex.org/I4390039268"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027132281","display_name":"Florent Forest","orcid":"https://orcid.org/0000-0001-6878-8752"},"institutions":[{"id":"https://openalex.org/I4210165303","display_name":"Safran (France)","ror":"https://ror.org/05sqf9v67","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210165303"]},{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH","FR"],"is_corresponding":false,"raw_author_name":"Florent Forest","raw_affiliation_strings":["EPFL,IMOS Lab,Lausanne,Switzerland","Safran Aircraft Engines (Courcouronnes - France)"],"affiliations":[{"raw_affiliation_string":"EPFL,IMOS Lab,Lausanne,Switzerland","institution_ids":["https://openalex.org/I5124864"]},{"raw_affiliation_string":"Safran Aircraft Engines (Courcouronnes - France)","institution_ids":["https://openalex.org/I4210165303"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047336564","display_name":"Hanane Azzag","orcid":"https://orcid.org/0000-0001-6876-0688"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210091279","display_name":"Universit\u00e9 Sorbonne Paris Nord","ror":"https://ror.org/0199hds37","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210091279"]},{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hanane Azzag","raw_affiliation_strings":["Sorbonne Paris Nord University,LIPN, UMR CNRS 7030,Villetaneuse,France"],"affiliations":[{"raw_affiliation_string":"Sorbonne Paris Nord University,LIPN, UMR CNRS 7030,Villetaneuse,France","institution_ids":["https://openalex.org/I4210156583","https://openalex.org/I1294671590","https://openalex.org/I4210091279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023422312","display_name":"Mustapha Lebbah","orcid":"https://orcid.org/0000-0001-7245-6371"},"institutions":[{"id":"https://openalex.org/I195731000","display_name":"Universit\u00e9 de Versailles Saint-Quentin-en-Yvelines","ror":"https://ror.org/03mkjjy25","country_code":"FR","type":"education","lineage":["https://openalex.org/I195731000","https://openalex.org/I277688954"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I4390039268","display_name":"Donn\u00e9es et algorithmes pour une ville intelligente et durable","ror":"https://ror.org/01xta2p78","country_code":"FR","type":"facility","lineage":["https://openalex.org/I195731000","https://openalex.org/I277688954","https://openalex.org/I4390039268"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Mustapha Lebbah","raw_affiliation_strings":["Paris-Saclay University,DAVID Lab, UVSQ,Versailles,France"],"affiliations":[{"raw_affiliation_string":"Paris-Saclay University,DAVID Lab, UVSQ,Versailles,France","institution_ids":["https://openalex.org/I195731000","https://openalex.org/I277688954","https://openalex.org/I4390039268"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063545602","display_name":"J\u00e9r\u00f4me Lacaille","orcid":"https://orcid.org/0000-0003-0743-025X"},"institutions":[{"id":"https://openalex.org/I4210165303","display_name":"Safran (France)","ror":"https://ror.org/05sqf9v67","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210165303"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jer\u00f4me Lacaille","raw_affiliation_strings":["Safran Aircraft Engines,DataLab,Moissy-Cramayel,France"],"affiliations":[{"raw_affiliation_string":"Safran Aircraft Engines,DataLab,Moissy-Cramayel,France","institution_ids":["https://openalex.org/I4210165303"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5034238046"],"corresponding_institution_ids":["https://openalex.org/I195731000","https://openalex.org/I277688954","https://openalex.org/I4390039268"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16345433,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9994999766349792,"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":0.9994999766349792,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9864000082015991,"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/T10320","display_name":"Neural Networks and Applications","score":0.9692000150680542,"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/multivariate-statistics","display_name":"Multivariate statistics","score":0.890837550163269},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6304962635040283},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5779925584793091},{"id":"https://openalex.org/keywords/multivariate-analysis","display_name":"Multivariate analysis","score":0.5548878908157349},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4102926254272461},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35635238885879517},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.35277360677719116},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27399611473083496},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2340046763420105},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.06033366918563843}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.890837550163269},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6304962635040283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5779925584793091},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.5548878908157349},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4102926254272461},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35635238885879517},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35277360677719116},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27399611473083496},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2340046763420105},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.06033366918563843},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651016","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-04713504v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04713504","pdf_url":null,"source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN), Jun 2024, Yokohama, France. pp.1-9, ⟨10.1109/IJCNN60899.2024.10651016⟩","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-04713504v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04713504","pdf_url":null,"source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN), Jun 2024, Yokohama, France. pp.1-9, ⟨10.1109/IJCNN60899.2024.10651016⟩","raw_type":"Conference papers"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320329943","display_name":"Safran Aircraft Engines","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2065454200","https://openalex.org/W2125389028","https://openalex.org/W2550143307","https://openalex.org/W2551393996","https://openalex.org/W2593414223","https://openalex.org/W2770173563","https://openalex.org/W2810632432","https://openalex.org/W2901593983","https://openalex.org/W2914120296","https://openalex.org/W2964268978","https://openalex.org/W2970360512","https://openalex.org/W2972810968","https://openalex.org/W2982438846","https://openalex.org/W3002709689","https://openalex.org/W3015694082","https://openalex.org/W3040508529","https://openalex.org/W3042807565","https://openalex.org/W3083255269","https://openalex.org/W3083891030","https://openalex.org/W3094502228","https://openalex.org/W3115948762","https://openalex.org/W3126170988","https://openalex.org/W3132890542","https://openalex.org/W3155381155","https://openalex.org/W4213397551","https://openalex.org/W4283716478","https://openalex.org/W4289360400","https://openalex.org/W4294583247","https://openalex.org/W4301206121","https://openalex.org/W4320013936","https://openalex.org/W4385245566","https://openalex.org/W4386320400","https://openalex.org/W6765779288","https://openalex.org/W6767737316","https://openalex.org/W6767782324","https://openalex.org/W6790346436"],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W40745829","https://openalex.org/W4318262572","https://openalex.org/W1978357124","https://openalex.org/W1578824628","https://openalex.org/W2032728545","https://openalex.org/W1570805059","https://openalex.org/W4250754046","https://openalex.org/W4243682621","https://openalex.org/W2036849593"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"exploring":[3],"deep":[4],"generative":[5,59],"models":[6,20],"for":[7,206],"generating":[8,225],"time":[9,44,170,189,207,228],"series":[10,45,190],"has":[11,46],"garnered":[12],"significant":[13],"interest":[14],"within":[15],"the":[16,34,42,51,67,75,81,105,153,200,211,214,220],"research":[17,218],"community.":[18],"These":[19],"have":[21],"found":[22],"wide-ranging":[23],"applications":[24],"in":[25,70,84,108,127,146,224],"areas":[26],"such":[27],"as":[28],"data":[29,120],"augmentation,":[30],"scenario":[31],"simulation,":[32],"and":[33,58,88,148,162,172],"imputation":[35],"of":[36,41,53,77,93,168,199,213,222],"missing":[37],"data.":[38,216],"The":[39,91],"authenticity":[40],"generated":[43,215],"seen":[47],"remarkable":[48],"advancements":[49],"with":[50,118],"integration":[52],"recurrent":[54],"neural":[55],"networks":[56,61],"(RNNs)":[57],"adversarial":[60],"(GANs).":[62],"RNNs":[63],"used":[64],"to":[65,103,124,209],"represent":[66],"state-of-the-art":[68],"(SOA)":[69],"processing":[71],"sequence":[72],"dependencies":[73],"until":[74],"advent":[76],"Transformers,":[78],"which":[79],"redefined":[80],"SOA,":[82],"especially":[83],"Natural":[85],"Language":[86],"Processing":[87],"Computer":[89],"Vision.":[90],"introduction":[92],"a":[94,134,157],"transformer-based":[95,144],"GAN":[96,140],"represented":[97],"an":[98,196],"innovative":[99,197],"step":[100],"forward,":[101],"aiming":[102],"address":[104],"limitations":[106],"inherent":[107],"RNNs.":[109],"However,":[110],"this":[111],"model\u2019s":[112],"efficacy":[113],"is":[114],"constrained":[115],"when":[116],"faced":[117],"unimodal":[119],"distribution":[121,129,175],"assumptions,":[122],"leading":[123],"arbitrary":[125],"outputs":[126],"complex":[128],"scenarios.":[130],"This":[131,217],"paper":[132],"introduces":[133],"novel":[135],"Multivariate":[136],"Time":[137],"Series":[138],"Conditional":[139],"(MTS-CGAN),":[141],"that":[142],"leverages":[143],"architectures":[145],"generator":[147],"discriminator":[149],"networks.":[150],"MTS-CGAN":[151,182,223],"conditions":[152],"generation":[154,167],"process":[155],"on":[156],"specific":[158],"encoded":[159],"context":[160],"(categorical":[161],"MTS":[163],"inputs),":[164],"enabling":[165],"one-pass":[166],"multivariate":[169,188,227],"series,":[171,208],"accommodating":[173],"mixed":[174],"frameworks,":[176],"outperforming":[177],"existing":[178],"models.":[179],"We":[180],"evaluate":[181],"using":[183],"quantitative":[184],"metrics":[185],"across":[186],"multiple":[187],"datasets.":[191],"Furthermore,":[192],"we":[193],"propose":[194],"also":[195],"adaptation":[198],"Frechet":[201],"Inception":[202],"Distance":[203],"(FID),":[204],"tailored":[205],"assess":[210],"quality":[212],"demonstrates":[219],"potential":[221],"high-fidelity":[226],"series.":[229]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}