{"id":"https://openalex.org/W7140132380","doi":"https://doi.org/10.48550/arxiv.2603.19970","title":"Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs","display_name":"Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W7140132380","doi":"https://doi.org/10.48550/arxiv.2603.19970"},"language":"en","primary_location":{"id":"pmh:oai:ris.utwente.nl:openaire/aa1a6553-6bdd-4b5e-b1fb-0357d7903f09","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/aa1a6553-6bdd-4b5e-b1fb-0357d7903f09","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Du, S, Rozanec, J M, Pimentel, A & Varbanescu, A-L 2026 'Graph2TS : Structure-Controlled Time Series Generation via Quantile-Graph VAEs' ArXiv.org. https://doi.org/10.48550/arXiv.2603.19970","raw_type":"info:eu-repo/semantics/preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.utwente.nl/en/publications/aa1a6553-6bdd-4b5e-b1fb-0357d7903f09","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130379805","display_name":"Shaoshuai Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Du, Shaoshuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067396725","display_name":"Jo\u017ee M. Ro\u017eanec","orcid":"https://orcid.org/0000-0002-3665-639X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rozanec, Joze M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008597247","display_name":"Andy Pimentel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pimentel, Andy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130349212","display_name":"Ana-Lucia Varbanescu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Varbanescu, Ana-Lucia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5130379805"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.526199996471405,"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"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.526199996471405,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.23350000381469727,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.07680000364780426,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/matching","display_name":"Matching (statistics)","score":0.5085999965667725},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4652000069618225},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4262999892234802},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4228000044822693},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.3880000114440918},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3878999948501587},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3723999857902527},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.36730000376701355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6100000143051147},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5085999965667725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5031999945640564},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4652000069618225},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4262999892234802},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4228000044822693},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4043999910354614},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.3880000114440918},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3878999948501587},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.36730000376701355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.328000009059906},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C37381756","wikidata":"https://www.wikidata.org/wiki/Q20203288","display_name":"Representativeness heuristic","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28369998931884766},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.27639999985694885},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.2718000113964081}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:ris.utwente.nl:openaire/aa1a6553-6bdd-4b5e-b1fb-0357d7903f09","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/aa1a6553-6bdd-4b5e-b1fb-0357d7903f09","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Du, S, Rozanec, J M, Pimentel, A & Varbanescu, A-L 2026 'Graph2TS : Structure-Controlled Time Series Generation via Quantile-Graph VAEs' ArXiv.org. https://doi.org/10.48550/arXiv.2603.19970","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2603.19970","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19970","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:ris.utwente.nl:openaire/aa1a6553-6bdd-4b5e-b1fb-0357d7903f09","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/aa1a6553-6bdd-4b5e-b1fb-0357d7903f09","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Du, S, Rozanec, J M, Pimentel, A & Varbanescu, A-L 2026 'Graph2TS : Structure-Controlled Time Series Generation via Quantile-Graph VAEs' ArXiv.org. https://doi.org/10.48550/arXiv.2603.19970","raw_type":"info:eu-repo/semantics/preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Although":[0],"recent":[1],"generative":[2],"models":[3],"can":[4,44],"produce":[5],"time":[6,128],"series":[7],"with":[8,33],"close":[9],"marginal":[10],"distributions,":[11],"they":[12],"often":[13],"face":[14],"a":[15,57,70,96,115,183],"fundamental":[16],"tension":[17],"between":[18],"preserving":[19],"global":[20,82,103,143],"temporal":[21,50,64,106,144,167],"structure":[22,94,134],"and":[23,73,105,160,169,174,179],"modeling":[24],"stochastic":[25,74,149],"local":[26],"variations,":[27],"particularly":[28],"for":[29,186],"highly":[30],"volatile":[31],"signals":[32],"weak":[34],"or":[35,47,138],"irregular":[36],"periodicity.":[37],"Direct":[38],"distribution":[39],"matching":[40],"in":[41],"such":[42],"settings":[43],"amplify":[45],"noise":[46],"suppress":[48],"meaningful":[49],"patterns.":[51],"In":[52],"this":[53,89,110],"work,":[54],"we":[55,91,112],"propose":[56,113],"structure-residual":[58],"perspective":[59],"on":[60,88,109,133,152],"time-series":[61,93,187],"generation,":[62],"viewing":[63],"data":[65],"as":[66,182],"the":[67,79,140],"combination":[68],"of":[69,81],"structural":[71,125],"backbone":[72],"residual":[75],"dynamics,":[76],"thereby":[77],"motivating":[78],"separation":[80],"organization":[83,145],"from":[84,124],"sample-level":[85],"variability.":[86],"Based":[87],"insight,":[90],"represent":[92],"using":[95],"quantile-based":[97],"transition":[98],"graph":[99],"that":[100,120],"compactly":[101],"captures":[102],"distributional":[104,165],"dependencies.":[107],"Building":[108],"representation,":[111],"Graph2TS,":[114],"quantile-graph":[116],"conditioned":[117],"variational":[118],"autoencoder":[119],"performs":[121],"cross-modal":[122,180],"generation":[123,132,181],"graphs":[126],"to":[127,172],"series.":[129],"By":[130],"conditioning":[131],"rather":[135],"than":[136],"labels":[137],"metadata,":[139],"model":[141],"preserves":[142],"while":[146],"enabling":[147],"controlled":[148],"variation.":[150],"Experiments":[151],"diverse":[153],"datasets,":[154],"including":[155],"sunspot,":[156],"electricity":[157],"load,":[158],"ECG,":[159],"EEG":[161],"signals,":[162],"demonstrate":[163],"improved":[164],"fidelity,":[166],"alignment,":[168],"representativeness":[170],"compared":[171],"diffusion-":[173],"GAN-based":[175],"baselines,":[176],"highlighting":[177],"structure-controlled":[178],"promising":[184],"direction":[185],"modeling.":[188]},"counts_by_year":[],"updated_date":"2026-05-25T08:39:21.599409","created_date":"2026-03-24T00:00:00"}
