{"id":"https://openalex.org/W4414079648","doi":"https://doi.org/10.1109/tkde.2025.3607005","title":"Financial Time Series Prediction With Multi-Granularity Graph Augmented Learning","display_name":"Financial Time Series Prediction With Multi-Granularity Graph Augmented Learning","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W4414079648","doi":"https://doi.org/10.1109/tkde.2025.3607005"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2025.3607005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2025.3607005","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"},"type":"article","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/A5040957883","display_name":"Peng Zhu","orcid":"https://orcid.org/0000-0001-9558-3787"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhu","raw_affiliation_strings":["School of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-9558-3787","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074886657","display_name":"Yuante Li","orcid":"https://orcid.org/0009-0002-2216-9651"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuante Li","raw_affiliation_strings":["School of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0002-2216-9651","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025515104","display_name":"Qinyuan Liu","orcid":"https://orcid.org/0000-0002-0170-3651"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinyuan Liu","raw_affiliation_strings":["School of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-0170-3651","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069869295","display_name":"Dawei Cheng","orcid":"https://orcid.org/0000-0002-5877-7387"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Cheng","raw_affiliation_strings":["School of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-5877-7387","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066099338","display_name":"Changjun Jiang","orcid":"https://orcid.org/0000-0003-0637-9317"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changjun Jiang","raw_affiliation_strings":["School of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-0637-9317","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":5.2775,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.95676299,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"37","issue":"11","first_page":"6436","last_page":"6449"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9883000254631042,"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":0.9883000254631042,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6079000234603882},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6000999808311462},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5127999782562256},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.39800000190734863},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.375900000333786},{"id":"https://openalex.org/keywords/financial-market","display_name":"Financial market","score":0.3727000057697296},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35429999232292175},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.35120001435279846},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.336899995803833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.848800003528595},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6079000234603882},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6000999808311462},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.583899974822998},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5127999782562256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.507099986076355},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39329999685287476},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C139043278","wikidata":"https://www.wikidata.org/wiki/Q837171","display_name":"Financial services","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.3100999891757965},{"id":"https://openalex.org/C540431452","wikidata":"https://www.wikidata.org/wiki/Q16319025","display_name":"FinTech","level":3,"score":0.29019999504089355},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C131562839","wikidata":"https://www.wikidata.org/wiki/Q1574928","display_name":"Trading strategy","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.2849999964237213},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27489998936653137},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2025.3607005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2025.3607005","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8582128816","display_name":null,"funder_award_id":"62472317","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W2020666351","https://openalex.org/W2125520394","https://openalex.org/W2346353015","https://openalex.org/W2613328025","https://openalex.org/W2734986640","https://openalex.org/W2951360122","https://openalex.org/W2965672544","https://openalex.org/W2973229164","https://openalex.org/W2995341844","https://openalex.org/W3014988774","https://openalex.org/W3019427697","https://openalex.org/W3035414307","https://openalex.org/W3158701715","https://openalex.org/W3172807453","https://openalex.org/W3173197429","https://openalex.org/W3175835345","https://openalex.org/W3190469032","https://openalex.org/W3203375555","https://openalex.org/W4224309822","https://openalex.org/W4283804236","https://openalex.org/W4306317752","https://openalex.org/W4378881184","https://openalex.org/W4385756443","https://openalex.org/W4386320397","https://openalex.org/W4390345459","https://openalex.org/W4392222741","https://openalex.org/W4393148033","https://openalex.org/W4393153055","https://openalex.org/W4393153100","https://openalex.org/W4393158552","https://openalex.org/W4396723154","https://openalex.org/W4396757512","https://openalex.org/W4396757543","https://openalex.org/W4400037040","https://openalex.org/W4401025027","https://openalex.org/W4401198529","https://openalex.org/W4401863207","https://openalex.org/W4403577789","https://openalex.org/W4403582610","https://openalex.org/W4403600951","https://openalex.org/W4404390753","https://openalex.org/W4404784447","https://openalex.org/W4405506917","https://openalex.org/W4407948952","https://openalex.org/W4409220872","https://openalex.org/W4409671317","https://openalex.org/W4411549706"],"related_works":["https://openalex.org/W2931688134","https://openalex.org/W2377919138","https://openalex.org/W2378857091","https://openalex.org/W103652678","https://openalex.org/W2166690231","https://openalex.org/W2950637221","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Financial":[0],"time":[1,46,143,157],"series":[2,47,144],"prediction":[3,207],"is":[4],"an":[5,188],"important":[6],"and":[7,20,29,162,176,221,246,252,259,270],"challenging":[8],"data":[9],"mining":[10],"task":[11],"for":[12,53,140],"quantitative":[13],"investment.":[14],"The":[15,225,254],"inherent":[16],"non-linearity,":[17],"high":[18],"noise,":[19],"susceptibility":[21],"to":[22,59,75,84,164,180,193],"various":[23],"factors,":[24],"such":[25,95],"as":[26,96,113],"macroeconomic":[27],"conditions":[28],"market":[30],"sentiment":[31],"in":[32,107,120,129,205,262,272],"the":[33,37,62,89,118,167,183,195,206,219],"stock":[34,85,223],"market,":[35],"increase":[36],"difficulty":[38],"of":[39,91,170],"prediction.":[40,54,86],"Existing":[41],"financial":[42,142,156,240],"industries":[43],"mainly":[44],"employ":[45],"models":[48],"or":[49],"fundamental":[50,199],"analysis":[51],"methods":[52,57,103,236],"However,":[55,87],"these":[56],"fail":[58],"effectively":[60,108,116],"capture":[61,166],"complex":[63],"interrelationships":[64],"between":[65],"equity.":[66],"In":[67],"recent":[68,92],"years,":[69],"graph":[70,136,153,190,196],"neural":[71],"networks":[72],"(GNNs),":[73],"due":[74],"their":[76],"powerful":[77],"relational":[78],"modeling":[79],"capabilities,":[80],"have":[81,105],"been":[82,257],"applied":[83,258],"with":[88,154,198],"advances":[90],"digital":[93],"power,":[94],"widely-used":[97],"high-frequency":[98],"trading":[99,264,274],"techniques,":[100],"existing":[101,234],"graph-based":[102],"still":[104],"shortcomings":[106],"learning":[109,138,197],"multi-granularity":[110,135,155],"temporal":[111,150],"relations":[112,169],"they":[114],"cannot":[115],"learn":[117],"patterns":[119],"different":[121],"frequencies,":[122],"e.g.,":[123],"minute-level,":[124,163],"daily,":[125,161],"weekly,":[126,160],"etc.":[127],"Therefore,":[128],"this":[130],"paper,":[131],"we":[132,186],"propose":[133],"a":[134,149],"augmented":[137],"framework":[139],"interrelated":[141],"forecasting.":[145],"We":[146,209],"first":[147],"construct":[148],"return":[151],"relationship":[152],"series,":[158],"including":[159,172,242],"comprehensively":[165],"dynamic":[168],"equities,":[171],"both":[173,218],"medium-term":[174],"trends":[175],"short-term":[177],"fluctuations.":[178],"Then,":[179],"further":[181,266],"augment":[182,191],"node":[184],"relations,":[185],"devise":[187],"attentional":[189],"module":[192],"improve":[194],"data,":[200],"which":[201],"are":[202],"jointly":[203],"optimized":[204],"layer.":[208],"conduct":[210],"extensive":[211],"empirical":[212],"studies":[213],"on":[214],"multiple":[215],"datasets":[216],"from":[217],"Chinese":[220],"U.S.":[222],"markets.":[224],"results":[226],"demonstrate":[227],"that":[228],"our":[229],"proposed":[230],"model":[231,255],"consistently":[232],"outperforms":[233],"baseline":[235],"across":[237],"four":[238],"key":[239],"metrics,":[241],"ARR,":[243],"ASR,":[244],"CR,":[245],"IR,":[247],"thereby":[248],"validating":[249],"its":[250,268],"effectiveness":[251],"superiority.":[253],"has":[256],"empirically":[260],"tested":[261],"commercial-grade":[263],"platforms,":[265],"demonstrating":[267],"efficiency":[269],"robustness":[271],"real-world":[273],"environments.":[275]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
