{"id":"https://openalex.org/W4402353801","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650090","title":"Fourier Graph Convolution Transformer for Financial Multivariate Time Series Forecasting","display_name":"Fourier Graph Convolution Transformer for Financial Multivariate Time Series Forecasting","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402353801","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650090"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650090","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650090","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":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101382904","display_name":"Junxian Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Junxian Zhou","raw_affiliation_strings":["University of Technology Sydney"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Technology Sydney","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082317196","display_name":"Shoujin Wang","orcid":"https://orcid.org/0000-0003-1133-9379"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shoujin Wang","raw_affiliation_strings":["University of Technology Sydney"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Technology Sydney","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056785578","display_name":"Yuming Ou","orcid":"https://orcid.org/0000-0001-5922-9406"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yuming Ou","raw_affiliation_strings":["University of Technology Sydney"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Technology Sydney","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9987999796867371,"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":0.9987999796867371,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9976999759674072,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"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/multivariate-statistics","display_name":"Multivariate statistics","score":0.6314101219177246},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5168622136116028},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4921845495700836},{"id":"https://openalex.org/keywords/fourier-series","display_name":"Fourier series","score":0.4896436333656311},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.43810051679611206},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.43441396951675415},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4279298782348633},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.319149374961853},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2293253242969513},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1184701919555664},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.10261628031730652},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.08094525337219238}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6314101219177246},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5168622136116028},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4921845495700836},{"id":"https://openalex.org/C207864730","wikidata":"https://www.wikidata.org/wiki/Q179467","display_name":"Fourier series","level":2,"score":0.4896436333656311},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.43810051679611206},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.43441396951675415},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4279298782348633},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.319149374961853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2293253242969513},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1184701919555664},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.10261628031730652},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.08094525337219238},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650090","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650090","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2061171222","https://openalex.org/W2064675550","https://openalex.org/W2592023122","https://openalex.org/W2744043447","https://openalex.org/W2893230400","https://openalex.org/W2964413206","https://openalex.org/W2969677753","https://openalex.org/W3038981236","https://openalex.org/W3080253043","https://openalex.org/W3080686571","https://openalex.org/W3099645789","https://openalex.org/W3123329971","https://openalex.org/W3127141647","https://openalex.org/W3139204882","https://openalex.org/W3175177406","https://openalex.org/W3192184597","https://openalex.org/W3208499031","https://openalex.org/W3212890323","https://openalex.org/W4210360375","https://openalex.org/W4249224151","https://openalex.org/W4280626475","https://openalex.org/W4300293005","https://openalex.org/W4303438998","https://openalex.org/W4319452384","https://openalex.org/W4321485380","https://openalex.org/W4385245566","https://openalex.org/W4385477955","https://openalex.org/W6767528804","https://openalex.org/W6780221082","https://openalex.org/W6786152982","https://openalex.org/W6797155008","https://openalex.org/W6803067837","https://openalex.org/W6810637551","https://openalex.org/W6838200255","https://openalex.org/W6845625448","https://openalex.org/W6849942326","https://openalex.org/W6858721613"],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W1991765889","https://openalex.org/W1990068454","https://openalex.org/W2472172556","https://openalex.org/W1570805059","https://openalex.org/W2357266745","https://openalex.org/W2094657987","https://openalex.org/W2122827492","https://openalex.org/W2320412982","https://openalex.org/W4387646299"],"abstract_inverted_index":{"Financial":[0],"Multivariate":[1,15],"Time":[2,16],"Series":[3,17],"(Fin-MTS)":[4],"forecasting":[5,44,90],"is":[6,115,199],"increasingly":[7],"critical":[8],"in":[9,65,79,83,95,138],"the":[10,68,75,123,133,144,150,155,170],"financial":[11],"market.":[12],"Unlike":[13],"other":[14],"(MTS)":[18],"data,":[19],"Fin-MTS":[20,43,110,139],"exhibits":[21],"particular":[22],"characteristics,":[23,49],"including":[24],"non-linearity,":[25],"volatility,":[26],"and":[27,53,70,87,112,125,158,173],"hidden":[28,47,135,156],"periodicities,":[29],"which":[30],"thus":[31,88],"introduce":[32,99],"great":[33],"challenges":[34],"for":[35,42,109],"modelling":[36,76,86,111],"it":[37],"well.":[38],"Existing":[39],"state-of-the-art":[40,194],"models":[41,60],"often":[45],"overlook":[46],"periodic":[48],"such":[50],"as":[51],"credit":[52],"monetary":[54],"policy":[55],"cycles.":[56],"More":[57],"importantly,":[58],"these":[59],"usually":[61],"show":[62],"limited":[63,89],"capability":[64],"well":[66,120,131,168],"capturing":[67],"intra-series":[69],"inter-series":[71,126,174],"dynamic":[72,127],"information":[73,81],"during":[74],"process,":[77],"resulting":[78],"significant":[80],"loss":[82],"quantitative":[84],"finance":[85],"performance.":[91],"To":[92],"this":[93,96,203],"end,":[94],"paper,":[97],"we":[98],"a":[100,161],"novel":[101,162],"model":[102,121],"called":[103],"Fourier":[104,163],"Graph":[105,164],"Convolution":[106,165],"Transformer":[107],"(FreTransformer)":[108],"forecasting.":[113],"FreTransformer":[114,141],"not":[116],"only":[117],"able":[118],"to":[119,153,167],"both":[122],"intra-":[124,172],"dependencies,":[128],"but":[129],"also":[130],"capture":[132,169],"important":[134],"periodicities":[136,157],"embedded":[137],"data.":[140],"first":[142],"maps":[143],"original":[145],"time":[146],"domain":[147,152],"data":[148,184],"into":[149],"frequency":[151],"disclose":[154],"then":[159],"employs":[160],"Network":[166],"complex":[171],"dependencies":[175],"within":[176],"Fin-MTS.":[177],"Extensive":[178],"experiments":[179],"on":[180],"real-world":[181],"US":[182],"market":[183],"across":[185],"12":[186],"phases":[187],"demonstrate":[188],"that":[189],"our":[190],"method":[191],"outperforms":[192],"current":[193],"models.":[195],"Our":[196],"source":[197],"code":[198],"publicly":[200],"available":[201],"at":[202],"repository:":[204],"https://github.com/AmsonntagChow/FreTransformer":[205]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}