{"id":"https://openalex.org/W4416017016","doi":"https://doi.org/10.1145/3746252.3761250","title":"From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets","display_name":"From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets","publication_year":2025,"publication_date":"2025-11-07","ids":{"openalex":"https://openalex.org/W4416017016","doi":"https://doi.org/10.1145/3746252.3761250"},"language":"en","primary_location":{"id":"doi:10.1145/3746252.3761250","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761250","pdf_url":null,"source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3746252.3761250","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108556179","display_name":"Ju-Won Kim","orcid":"https://orcid.org/0009-0000-5176-4607"},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Juwon Kim","raw_affiliation_strings":["Pohang University of Science and Technology, Pohang, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0000-5176-4607","affiliations":[{"raw_affiliation_string":"Pohang University of Science and Technology, Pohang, Republic of Korea","institution_ids":["https://openalex.org/I123900574"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101907560","display_name":"Hyunwook Lee","orcid":"https://orcid.org/0000-0002-5506-7347"},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunwook Lee","raw_affiliation_strings":["Pohang University of Science and Technology, Pohang, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-5506-7347","affiliations":[{"raw_affiliation_string":"Pohang University of Science and Technology, Pohang, Republic of Korea","institution_ids":["https://openalex.org/I123900574"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110593495","display_name":"Hyun K. Jeon","orcid":"https://orcid.org/0009-0006-0030-6677"},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyotaek Jeon","raw_affiliation_strings":["Pohang University of Science and Technology, Pohang, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0006-0030-6677","affiliations":[{"raw_affiliation_string":"Pohang University of Science and Technology, Pohang, Republic of Korea","institution_ids":["https://openalex.org/I123900574"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026203914","display_name":"Seungmin Jin","orcid":"https://orcid.org/0000-0002-8242-6157"},"institutions":[{"id":"https://openalex.org/I48566637","display_name":"Ulsan National Institute of Science and Technology","ror":"https://ror.org/017cjz748","country_code":"KR","type":"education","lineage":["https://openalex.org/I48566637"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seungmin Jin","raw_affiliation_strings":["Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-8242-6157","affiliations":[{"raw_affiliation_string":"Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea","institution_ids":["https://openalex.org/I48566637"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020791375","display_name":"Sungahn Ko","orcid":"https://orcid.org/0000-0002-7410-5652"},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungahn Ko","raw_affiliation_strings":["Pohang University of Science and Technology, Pohang, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-7410-5652","affiliations":[{"raw_affiliation_string":"Pohang University of Science and Technology, Pohang, Republic of Korea","institution_ids":["https://openalex.org/I123900574"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"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":"1344","last_page":"1353"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.4936000108718872,"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":0.4936000108718872,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.25450000166893005,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.07779999822378159,"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/ambiguity","display_name":"Ambiguity","score":0.5590999722480774},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4471000134944916},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4320000112056732},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.42719998955726624},{"id":"https://openalex.org/keywords/financial-market","display_name":"Financial market","score":0.41749998927116394},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4090999960899353},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.38269999623298645},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.3546000123023987}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.654699981212616},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.649399995803833},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.5590999722480774},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4683000147342682},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4471000134944916},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.41749998927116394},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4090999960899353},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.38269999623298645},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.3546000123023987},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3483999967575073},{"id":"https://openalex.org/C2776825360","wikidata":"https://www.wikidata.org/wiki/Q1411921","display_name":"Vagueness","level":3,"score":0.34119999408721924},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C135593079","wikidata":"https://www.wikidata.org/wiki/Q1750766","display_name":"Scale invariance","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2858000099658966},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.27250000834465027},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.26660001277923584},{"id":"https://openalex.org/C536366893","wikidata":"https://www.wikidata.org/wiki/Q66076","display_name":"Foreign exchange market","level":3,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3746252.3761250","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761250","pdf_url":null,"source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2509.15040","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.15040","pdf_url":"https://arxiv.org/pdf/2509.15040","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3746252.3761250","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761250","pdf_url":null,"source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8296148319","display_name":null,"funder_award_id":"No.RS-2019-II191906","funder_id":"https://openalex.org/F4320321282","funder_display_name":"Pohang University of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320321282","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1971713783","https://openalex.org/W1978371851","https://openalex.org/W2029438113","https://openalex.org/W2081028405","https://openalex.org/W2744043447","https://openalex.org/W3123777129","https://openalex.org/W3126162951","https://openalex.org/W3173748501","https://openalex.org/W3207999419","https://openalex.org/W4372329392","https://openalex.org/W4382203079","https://openalex.org/W4382239610","https://openalex.org/W4393147794","https://openalex.org/W4393158552","https://openalex.org/W4401023833","https://openalex.org/W4408355063"],"related_works":[],"abstract_inverted_index":{"Directional":[0],"forecasting":[1],"in":[2,131],"financial":[3],"markets":[4],"requires":[5],"both":[6],"accuracy":[7],"and":[8,28,68,82,106,118],"interpretability.":[9],"Before":[10],"the":[11,98,160],"advent":[12],"of":[13,101,134],"deep":[14,35,143],"learning,":[15],"interpretable":[16,63,151],"approaches":[17],"based":[18],"on":[19,116],"human-defined":[20],"patterns":[21,75,103],"were":[22],"prevalent,":[23],"but":[24],"their":[25],"structural":[26],"vagueness":[27],"scale":[29],"ambiguity":[30],"hindered":[31],"generalization.":[32],"In":[33],"contrast,":[34],"learning":[36,144],"models":[37,145],"can":[38],"effectively":[39],"capture":[40],"complex":[41],"dynamics,":[42],"yet":[43],"often":[44],"offer":[45],"limited":[46],"transparency.":[47],"To":[48],"bridge":[49],"this":[50],"gap,":[51],"we":[52],"propose":[53],"a":[54,93],"two-stage":[55],"framework":[56],"that":[57,76,96,124,146,164],"integrates":[58],"unsupervised":[59],"pattern":[60,162],"extracion":[61],"with":[62],"forecasting.":[64],"(i)":[65],"SIMPC":[66],"segments":[67],"clusters":[69],"multivariate":[70],"time":[71],"series,":[72],"extracting":[73],"recurrent":[74],"are":[77],"invariant":[78],"to":[79],"amplitude":[80],"scaling":[81],"temporal":[83],"distortion,":[84],"even":[85],"under":[86],"varying":[87],"window":[88],"sizes.":[89],"(ii)":[90],"JISC-Net":[91],"is":[92],"shapelet-based":[94],"classifier":[95],"uses":[97],"initial":[99],"part":[100],"extracted":[102],"as":[104],"input":[105],"forecasts":[107],"subsequent":[108],"partial":[109],"sequences":[110],"for":[111],"short-term":[112],"directional":[113],"movement.":[114],"Experiments":[115],"Bitcoin":[117],"three":[119],"S&P":[120],"500":[121],"equities":[122],"demonstrate":[123],"our":[125,153],"method":[126],"ranks":[127],"first":[128],"or":[129],"second":[130],"11":[132],"out":[133],"12":[135],"metric--dataset":[136],"combinations,":[137],"consistently":[138],"outperforming":[139],"baselines.":[140],"Unlike":[141],"conventional":[142],"output":[147],"buy-or-sell":[148],"signals":[149],"without":[150],"justification,":[152],"approach":[154],"enables":[155],"transparent":[156],"decision-making":[157],"by":[158],"revealing":[159],"underlying":[161],"structures":[163],"drive":[165],"predictive":[166],"outcomes.":[167]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}