{"id":"https://openalex.org/W4406495587","doi":"https://doi.org/10.1109/bigdata62323.2024.10825171","title":"Accurate Stock Movement Prediction via Multi-Scale and Multi-Domain Modeling","display_name":"Accurate Stock Movement Prediction via Multi-Scale and Multi-Domain Modeling","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406495587","doi":"https://doi.org/10.1109/bigdata62323.2024.10825171"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825171","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-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/A5107886291","display_name":"J. Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"JinGee Kim","raw_affiliation_strings":["Seoul National University,Seoul,Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Seoul National University,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069090005","display_name":"Yong-Chan Park","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yong-Chan Park","raw_affiliation_strings":["Seoul National University,Seoul,Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Seoul National University,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101425075","display_name":"Jaemin Hong","orcid":"https://orcid.org/0009-0000-0690-296X"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaemin Hong","raw_affiliation_strings":["Seoul National University,Seoul,Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Seoul National University,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074561505","display_name":"U Kang","orcid":"https://orcid.org/0000-0002-8774-6950"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"U Kang","raw_affiliation_strings":["Seoul National University,Seoul,Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Seoul National University,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5107886291"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":1.5428,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.85622646,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1795","last_page":"1803"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.996999979019165,"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":0.996999979019165,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.989799976348877,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9735000133514404,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6677086353302002},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5101661682128906},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.06828626990318298},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.05711573362350464}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6677086353302002},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5101661682128906},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.06828626990318298},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.05711573362350464}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825171","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321292","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W2044271574","https://openalex.org/W2061171222","https://openalex.org/W2064675550","https://openalex.org/W2070473414","https://openalex.org/W2095705004","https://openalex.org/W2115839797","https://openalex.org/W2133564696","https://openalex.org/W2135461721","https://openalex.org/W2168353136","https://openalex.org/W2306394264","https://openalex.org/W2468992649","https://openalex.org/W2613328025","https://openalex.org/W2741951152","https://openalex.org/W2744043447","https://openalex.org/W2798413829","https://openalex.org/W2896309423","https://openalex.org/W2922268266","https://openalex.org/W2963756544","https://openalex.org/W2964413206","https://openalex.org/W2990838508","https://openalex.org/W3017229010","https://openalex.org/W3035275162","https://openalex.org/W3035414307","https://openalex.org/W3103157245","https://openalex.org/W3110420963","https://openalex.org/W3123598380","https://openalex.org/W3124500271","https://openalex.org/W3166456567","https://openalex.org/W3169540234","https://openalex.org/W3170487013","https://openalex.org/W3197435135","https://openalex.org/W3213601271","https://openalex.org/W4250936085","https://openalex.org/W4300293005","https://openalex.org/W4301893865","https://openalex.org/W4318148071","https://openalex.org/W4366828973","https://openalex.org/W4385245566","https://openalex.org/W4388651085","https://openalex.org/W4401863207","https://openalex.org/W4401863236","https://openalex.org/W6674330103","https://openalex.org/W6679434410","https://openalex.org/W6698169118","https://openalex.org/W6720026311","https://openalex.org/W6758805977","https://openalex.org/W6772412004","https://openalex.org/W6780226713","https://openalex.org/W6797578546","https://openalex.org/W6858342418"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"How":[0],"can":[1],"we":[2,65,118],"utilize":[3],"historical":[4,25],"stock":[5,9,23,26,36,61,72,80,134,163],"prices":[6],"for":[7,71],"accurate":[8,69],"movement":[10,20,73],"prediction?":[11],"There":[12],"have":[13],"been":[14],"several":[15],"attempts":[16],"to":[17,30,41,129,138,165,182],"predict":[18,43],"the":[19,31,35,44,60,148,167],"of":[21,34,47,169],"a":[22,107,113],"using":[24],"prices.":[27],"However,":[28],"due":[29],"erratic":[32],"nature":[33],"market,":[37],"it":[38],"is":[39],"difficult":[40],"accurately":[42],"future":[45],"trajectory":[46],"stocks.":[48],"Existing":[49],"works":[50],"are":[51],"limited":[52],"in":[53,79,121,141,147,184,188],"capturing":[54],"complex":[55,77],"dependencies":[56,99],"that":[57,174],"exist":[58],"within":[59],"data.In":[62],"this":[63],"paper,":[64],"propose":[66],"ZoomStock,":[67],"an":[68],"method":[70],"prediction.":[74],"ZoomStock":[75,89,125,144,175],"captures":[76,90],"patterns":[78,92,120,132,140,146],"price":[81],"data":[82,105],"with":[83],"multi-scale":[84,91,104],"and":[85,97,111,186],"multi-domain":[86,127],"modeling.":[87],"First,":[88],"by":[93,180],"examining":[94],"short-term,":[95],"mid-term,":[96],"long-term":[98],"from":[100,133,161],"data.":[101,135],"By":[102],"feeding":[103],"into":[106],"1D":[108],"convolution":[109,115],"layer":[110],"applying":[112],"circular":[114],"on":[116,158],"data,":[117],"learn":[119],"various":[122],"scales.":[123],"Second,":[124],"leverages":[126],"features":[128],"extract":[130],"diverse":[131,162],"In":[136],"addition":[137],"finding":[139],"time":[142],"domain,":[143],"exploits":[145],"frequency":[149],"domain":[150],"via":[151],"Fourier":[152],"Transform.":[153],"We":[154],"conduct":[155],"extensive":[156],"experiments":[157],"real-world":[159],"datasets":[160],"markets":[164],"evaluate":[166],"effectiveness":[168],"ZoomStock.":[170],"Our":[171],"results":[172],"demonstrate":[173],"outperforms":[176],"previous":[177],"state-of-the-art":[178],"models":[179],"up":[181],"15.7%p":[183],"accuracy":[185],"31.7%p":[187],"MCC,":[189],"showcasing":[190],"its":[191],"superior":[192],"predictive":[193],"capabilities.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}