{"id":"https://openalex.org/W6889879050","doi":"https://doi.org/10.26204/kluedo/8707","title":"Generative Adversarial Networks for Time Series Simulation","display_name":"Generative Adversarial Networks for Time Series Simulation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W6889879050","doi":"https://doi.org/10.26204/kluedo/8707"},"language":"en","primary_location":{"id":"pmh:oai:kluedo.ub.rptu.de:8707","doi":null,"is_oa":true,"landing_page_url":"https://kluedo.ub.rptu.de/frontdoor/index/index/docId/8707","pdf_url":null,"source":{"id":"https://openalex.org/S4306402046","display_name":"Publication Server of Kaiserslautern University of Technology (Kaiserslautern University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I153267046","host_organization_name":"University of Kaiserslautern","host_organization_lineage":["https://openalex.org/I153267046"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doctoralthesis"},"volume":null,"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://kluedo.ub.rptu.de/frontdoor/index/index/docId/8707","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117363602","display_name":"Laurena Ramadani","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ramadani, Laurena","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5117363602"],"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":true,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.5741999745368958,"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"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.5741999745368958,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.3522999882698059,"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/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.02630000002682209,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generative-adversarial-networks","display_name":"generative adversarial networks","score":0.9800000190734863},{"id":"https://openalex.org/keywords/time-series-simulation","display_name":"time series simulation","score":0.8090000152587891},{"id":"https://openalex.org/keywords/synthetic-financial-data","display_name":"synthetic financial data","score":0.7770000100135803},{"id":"https://openalex.org/keywords/timegan","display_name":"TimeGAN","score":0.9950000047683716},{"id":"https://openalex.org/keywords/conditional-gan","display_name":"conditional generative adversarial networks","score":0.859000027179718},{"id":"https://openalex.org/keywords/load-forecasting","display_name":"load forecasting","score":0.8140000104904175},{"id":"https://openalex.org/keywords/s-p-500","display_name":"S&P 500","score":0.9399999976158142},{"id":"https://openalex.org/keywords/value-at-risk","display_name":"value at risk","score":0.6959999799728394},{"id":"https://openalex.org/keywords/portfolio-optimization","display_name":"portfolio optimization","score":0.9959999918937683},{"id":"https://openalex.org/keywords/markowitz-mean-variance-model","display_name":"Markowitz mean-variance model","score":0.859000027179718},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"principal component analysis","score":0.7720000147819519},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.5090000033378601},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.7239999771118164},{"id":"https://openalex.org/keywords/outlier-handling","display_name":"outlier handling","score":0.6000000238418579},{"id":"https://openalex.org/keywords/data-transformation","display_name":"data transformation","score":0.6060000061988831},{"id":"https://openalex.org/keywords/stock-returns","display_name":"stock returns","score":0.36899998784065247},{"id":"https://openalex.org/keywords/nonstationary-time-series","display_name":"non-stationary time series","score":0.6489999890327454}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7195000052452087},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.6103000044822693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5430999994277954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5353999733924866},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5029000043869019},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5006999969482422},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.4968000054359436},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.49320000410079956},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4771000146865845},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.4713999927043915},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4593999981880188},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43709999322891235},{"id":"https://openalex.org/C202655437","wikidata":"https://www.wikidata.org/wiki/Q7231728","display_name":"Portfolio optimization","level":3,"score":0.4002000093460083},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C94128290","wikidata":"https://www.wikidata.org/wiki/Q963287","display_name":"Value at risk","level":3,"score":0.34439998865127563},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.310699999332428},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C114118609","wikidata":"https://www.wikidata.org/wiki/Q3036837","display_name":"Market data","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.26910001039505005},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.26080000400543213}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:kluedo.ub.rptu.de:8707","doi":null,"is_oa":true,"landing_page_url":"https://kluedo.ub.rptu.de/frontdoor/index/index/docId/8707","pdf_url":null,"source":{"id":"https://openalex.org/S4306402046","display_name":"Publication Server of Kaiserslautern University of Technology (Kaiserslautern University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I153267046","host_organization_name":"University of Kaiserslautern","host_organization_lineage":["https://openalex.org/I153267046"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doctoralthesis"},{"id":"doi:10.26204/kluedo/8707","doi":"https://doi.org/10.26204/kluedo/8707","is_oa":true,"landing_page_url":"https://doi.org/10.26204/kluedo/8707","pdf_url":null,"source":{"id":"https://openalex.org/S7407052988","display_name":"Technische Universit\u00e4t Kaiserslautern","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"thesis"}],"best_oa_location":{"id":"pmh:oai:kluedo.ub.rptu.de:8707","doi":null,"is_oa":true,"landing_page_url":"https://kluedo.ub.rptu.de/frontdoor/index/index/docId/8707","pdf_url":null,"source":{"id":"https://openalex.org/S4306402046","display_name":"Publication Server of Kaiserslautern University of Technology (Kaiserslautern University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I153267046","host_organization_name":"University of Kaiserslautern","host_organization_lineage":["https://openalex.org/I153267046"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doctoralthesis"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0,7],"Adversarial":[1,8],"Networks":[2,9],"for":[3,72,203,327,335,359,454,498,510],"Time":[4],"Series":[5],"Simulation":[6],"(GANs),":[10],"first":[11,128],"introduced":[12],"by":[13,26,114,160,369,468],"Ian":[14],"Goodfellow":[15],"in":[16,108,216,238,271,284,371,401,432,520],"2014,":[17],"have":[18],"revolutionized":[19],"the":[20,28,43,100,104,127,145,147,172,174,180,190,210,239,256,261,264,320,342,350,356,380,399,425,442,460,478,511,529],"architecture":[21],"of":[22,30,45,84,92,103,171,176,182,214,220,241,263,324,382,462],"deep":[23],"neural":[24],"networks":[25],"enabling":[27],"generation":[29,44],"realistic":[31],"data":[32,118,133,158,177,291,303,344,365,368,400,464,467,471,488,533],"across":[33,414],"various":[34,73],"domains.":[35],"In":[36,76,224,306],"this":[37,77],"study,":[38,78],"we":[39,79,230,300,313,386,458],"apply":[40],"GANs":[41,94,334,370],"to":[42,50,58,98,123,188,197,208,226,234,292,308,319,354,373,397,408,490],"synthetic":[46,470],"financial":[47,74,157,235,522],"data,":[48,229,513],"aiming":[49],"replicate":[51],"real-world":[52],"patterns.":[53],"This":[54,525],"approach":[55],"allows":[56],"us":[57,207],"extend":[59],"limited":[60],"historical":[61,117,290,466],"datasets,":[62,205],"train":[63],"machine":[64],"learning":[65],"models":[66],"more":[67,139,451,475,536],"effectively,":[68],"and":[69,87,141,144,162,184,212,247,260,281,330,339,366,412,477,505,518],"conduct":[70],"simulations":[71],"applications.":[75],"focus":[80],"on":[81,116,362,441],"two":[82,124],"types":[83,219],"GANs:":[85],"ForGAN":[86,122,215,383],"TimeGAN.":[88,391],"ForGAN,":[89],"a":[90,109,138,402,450],"variant":[91],"Conditional":[93],"(cGAN),":[95],"is":[96,274,288,481],"designed":[97],"model":[99],"probability":[101],"distribution":[102],"one-step":[105],"ahead":[106],"value":[107],"time":[110,201,221,415],"series,":[111],"specifically":[112],"xt+1":[113],"conditioning":[115],"x0,x1,...,xt.":[119],"We":[120],"applied":[121,314],"distinct":[125,218],"datasets:":[126],"being":[129],"15-minute":[130],"electricity":[131],"consumption":[132],"from":[134,345],"Germany\u2019s":[135],"intraday":[136],"market,":[137],"regular":[140],"seasonal":[142],"dataset;":[143],"second,":[146],"Standard":[148],"&":[149],"Poor\u2019s":[150],"500":[151],"(S&P":[152],"500)":[153],"index,":[154],"which":[155],"represents":[156],"characterized":[159],"volatility":[161],"non-stationarity.":[163],"Our":[164,483],"analysis":[165,484],"involved":[166],"addressing":[167],"several":[168],"key":[169,276,499],"aspects":[170],"data:":[173],"impact":[175],"transformation":[178],"techniques,":[179],"treatment":[181],"outliers,":[183],"extensive":[185],"hyperparameter":[186],"tuning":[187],"enhance":[189],"model\u2019s":[191],"performance.":[192,376],"These":[193],"adjustments":[194],"were":[195,254,507],"essential":[196],"accurately":[198,293],"simulate":[199],"future":[200,228],"periods":[202],"both":[204],"allowing":[206,406],"assess":[209],"strengths":[211],"limitations":[213],"handling":[217],"series":[222],"data.":[223],"addition":[225],"generating":[227],"extended":[231],"our":[232],"study":[233],"applications,":[236],"particularly":[237,431],"areas":[240],"Value":[242],"at":[243,421],"Risk":[244],"(VaR)":[245],"estimation":[246],"portfolio":[248,251,272,360,455,492],"optimization.":[249,456],"Two":[250],"optimization":[252,273],"approaches":[253],"examined:":[255],"Markowitz":[257],"mean-variance":[258],"method":[259],"Maximization":[262],"Expected":[265],"Growth":[266],"Rate.":[267],"A":[268],"crucial":[269],"task":[270],"estimating":[275],"parameters":[277,357],"such":[278,501],"as":[279,502],"means":[280],"covariances.":[282],"However,":[283,417],"many":[285],"cases,":[286],"there":[287],"insufficient":[289],"estimate":[294,355],"these":[295,447],"parameters.":[296],"To":[297,377],"address":[298],"this,":[299],"generated":[301,343],"additional":[302],"using":[304,469],"ForGAN.":[305],"order":[307,372],"preserve":[309],"correlations":[310,434],"between":[311,435],"assets,":[312],"Principal":[315],"Component":[316],"Analysis":[317],"(PCA)":[318],"joint":[321],"log":[322,351],"returns":[323],"stock":[325],"prices":[326],"Amazon,":[328],"Apple,":[329],"Netflix,":[331],"trained":[332],"separate":[333],"each":[336,346],"principal":[337],"component,":[338],"then":[340],"transformed":[341],"component":[347],"back":[348],"into":[349],"return":[352],"space":[353],"relevant":[358],"optimization,":[361],"standard":[363],"training":[364],"simulated":[367],"compare":[374,379],"their":[375],"further":[378],"performance":[381],"with":[384,439],"PCA,":[385,440],"employed":[387],"another":[388],"GAN":[389,438],"model,":[390],"TimeGAN":[392,419],"uses":[393],"an":[394],"embedding":[395],"network":[396],"represent":[398],"lower":[403],"dimensional":[404],"space,":[405],"it":[407],"capture":[409],"temporal":[410,423],"dependencies":[411],"relationships":[413],"points.":[416],"while":[418],"excels":[420],"preserving":[422],"patterns,":[424],"dimensionality":[426],"reduction":[427],"can":[428,534],"introduce":[429],"inaccuracies,":[430],"retaining":[433],"assets.":[436],"For":[437],"other":[443],"hand,":[444],"effectively":[445],"maintains":[446],"correlations,":[448],"providing":[449],"reliable":[452,537],"framework":[453],"Finally,":[457],"evaluated":[459],"reliability":[461,519],"GAN-generated":[463,487,512,532],"against":[465],"where":[472],"conditions":[473],"are":[474],"controlled":[476],"exact":[479],"solution":[480],"known.":[482],"demonstrated":[485],"that":[486],"led":[489],"improved":[491],"allocations.":[493],"Additionally,":[494],"confidence":[495],"intervals":[496],"(CIs)":[497],"parameters,":[500],"drift,":[503],"volatility,":[504],"correlation,":[506],"generally":[508],"narrower":[509],"indicating":[514],"potentially":[515],"higher":[516],"precision":[517],"certain":[521],"modeling":[523],"settings.":[524],"suggests":[526],"that,":[527],"under":[528],"right":[530],"circumstances,":[531],"provide":[535],"estimates.":[538]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2025-10-10T00:00:00"}
