{"id":"https://openalex.org/W4415035781","doi":"https://doi.org/10.52202/085713-5750","title":"Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach","display_name":"Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415035781","doi":"https://doi.org/10.52202/085713-5750"},"language":"en","primary_location":{"id":"doi:10.52202/085713-5750","doi":"https://doi.org/10.52202/085713-5750","is_oa":false,"landing_page_url":"https://doi.org/10.52202/085713-5750","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Neural Information Processing Systems 38","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2505.20446","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119938265","display_name":"Tal Gonen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Tal Gonen","raw_affiliation_strings":["Ben Gurion University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ben Gurion University","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114619529","display_name":"Itai Pemper","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Itai Pemper","raw_affiliation_strings":["Ben Gurion University of the Negev"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ben Gurion University of the Negev","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003433915","display_name":"Ilan Naiman","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Ilan Naiman","raw_affiliation_strings":["Ben Gurion University Google X"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ben Gurion University Google X","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004888329","display_name":"Nimrod Berman","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nimrod Berman","raw_affiliation_strings":["Amazon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010203098","display_name":"Omri Azencot","orcid":"https://orcid.org/0000-0001-9407-6601","observed_orcids":["https://orcid.org/0000-0001-9407-6601"]},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Omri Azencot","raw_affiliation_strings":["Ben-Gurion University of the Negev"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ben-Gurion University of the Negev","institution_ids":["https://openalex.org/I124227911"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.8443,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.93905536,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"191446","last_page":"191485"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9878000020980835,"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":0.9878000020980835,"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":0.007600000128149986,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0010999999940395355,"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/time-series-generation","display_name":"time series generation","score":0.9470000267028809},{"id":"https://openalex.org/keywords/data-scarcity","display_name":"data scarcity","score":0.8659999966621399},{"id":"https://openalex.org/keywords/few-shot-learning","display_name":"few-shot learning","score":0.8610000014305115},{"id":"https://openalex.org/keywords/diffusion-models","display_name":"diffusion models","score":0.9210000038146973},{"id":"https://openalex.org/keywords/generative-models","display_name":"generative models","score":0.6869999766349792},{"id":"https://openalex.org/keywords/pretraining","display_name":"pre-training","score":0.8159999847412109},{"id":"https://openalex.org/keywords/domain-generalization","display_name":"domain generalization","score":0.949999988079071},{"id":"https://openalex.org/keywords/temporal-representation","display_name":"representation of time","score":0.48399999737739563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7918999791145325},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7811999917030334},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6306999921798706},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5763000249862671},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.49549999833106995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48500001430511475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4749000072479248},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4578000009059906},{"id":"https://openalex.org/C184408114","wikidata":"https://www.wikidata.org/wiki/Q1502022","display_name":"Generative Design","level":3,"score":0.42340001463890076},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.40059998631477356},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.35910001397132874},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3043000102043152},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2606000006198883}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.52202/085713-5750","doi":"https://doi.org/10.52202/085713-5750","is_oa":false,"landing_page_url":"https://doi.org/10.52202/085713-5750","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Neural Information Processing Systems 38","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2505.20446","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.20446","pdf_url":"https://arxiv.org/pdf/2505.20446","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2505.20446","doi":"https://doi.org/10.48550/arxiv.2505.20446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2505.20446","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2505.20446","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.20446","pdf_url":"https://arxiv.org/pdf/2505.20446","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322252","display_name":"Israel Science Foundation","ror":"https://ror.org/04sazxf24"},{"id":"https://openalex.org/F4320323051","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982"},{"id":"https://openalex.org/F4320323278","display_name":"Council for Higher Education","ror":"https://ror.org/036mqp197"},{"id":"https://openalex.org/F4320337393","display_name":"Division of Chemistry","ror":"https://ror.org/01ar8dr59"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415035781.pdf","grobid_xml":"https://content.openalex.org/works/W4415035781.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W2898647012","https://openalex.org/W3195577433","https://openalex.org/W3211470795","https://openalex.org/W4322718191","https://openalex.org/W4390041933","https://openalex.org/W4403160018","https://openalex.org/W7136179659"],"related_works":["https://openalex.org/W7164908252","https://openalex.org/W7127342940","https://openalex.org/W7161745081","https://openalex.org/W4391590962","https://openalex.org/W4417040049","https://openalex.org/W7131419230","https://openalex.org/W7124818313","https://openalex.org/W7164188145","https://openalex.org/W4409347072","https://openalex.org/W7135208819"],"abstract_inverted_index":{"Generative":[0],"modeling":[1,183],"of":[2,26,97,148,167],"time":[3,10,78,98,189],"series":[4,11,79,99,190],"is":[5,90,202],"a":[6,23,54,69,85,93,145,185],"central":[7],"challenge":[8],"in":[9,20,50,139,188],"analysis,":[12],"particularly":[13],"under":[14,32],"data-scarce":[15,51,61],"conditions.":[16],"Despite":[17],"recent":[18],"advances":[19],"generative":[21,29,48,72,182],"modeling,":[22],"comprehensive":[24],"understanding":[25],"how":[27],"state-of-the-art":[28,137],"models":[30,49],"perform":[31],"limited":[33],"supervision":[34],"remains":[35],"lacking.":[36],"In":[37],"this":[38,65,174],"work,":[39],"we":[40,67],"conduct":[41],"the":[42,165,177],"first":[43],"large-scale":[44],"study":[45],"evaluating":[46],"leading":[47],"settings,":[52],"revealing":[53],"substantial":[55],"performance":[56,138],"gap":[57],"between":[58],"full-data":[59],"and":[60,122,169,192],"regimes.":[62],"To":[63],"close":[64],"gap,":[66],"propose":[68],"unified":[70,134,194],"diffusion-based":[71],"framework":[73],"that":[74,196],"can":[75],"synthesize":[76],"high-fidelity":[77],"across":[80,144,199],"diverse":[81],"domains":[82],"using":[83],"just":[84],"few":[86],"examples.":[87],"Our":[88],"model":[89,135],"pre-trained":[91],"on":[92,160],"large,":[94],"heterogeneous":[95],"collection":[96],"datasets,":[100],"enabling":[101],"it":[102,152],"to":[103,179],"learn":[104],"generalizable":[105],"temporal":[106],"representations.":[107],"It":[108],"further":[109],"incorporates":[110],"architectural":[111],"innovations":[112],"such":[113],"as":[114,184],"dynamic":[115],"convolutional":[116],"layers":[117],"for":[118,126],"flexible":[119],"channel":[120],"adaptation":[121],"dataset":[123],"token":[124],"conditioning":[125],"domain-aware":[127],"generation.":[128],"Without":[129],"requiring":[130],"abundant":[131],"supervision,":[132],"our":[133],"achieves":[136],"few-shot":[140,181],"settings-outperforming":[141],"domain-specific":[142],"baselines":[143,156],"wide":[146],"range":[147],"subset":[149],"sizes.":[150],"Remarkably,":[151],"also":[153],"surpasses":[154],"all":[155],"even":[157],"when":[158],"tested":[159],"full":[161],"datasets":[162],"benchmarks,":[163],"highlighting":[164],"strength":[166],"pre-training":[168],"cross-domain":[170],"generalization.":[171],"We":[172],"hope":[173],"work":[175],"encourages":[176],"community":[178],"revisit":[180],"key":[186],"problem":[187],"research":[191],"pursue":[193],"solutions":[195],"scale":[197],"efficiently":[198],"domains.":[200],"Code":[201],"available":[203],"at":[204],"https://github.com/azencot-group/ImagenFew.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2025-10-10T00:00:00"}