{"id":"https://openalex.org/W7165638262","doi":"https://doi.org/10.1609/aaaiss.v9i1.42925","title":"Multimodal Forecasting for Commodity Prices Using Spectrogram-Based and Time Series Representations","display_name":"Multimodal Forecasting for Commodity Prices Using Spectrogram-Based and Time Series Representations","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165638262","doi":"https://doi.org/10.1609/aaaiss.v9i1.42925"},"language":"en","primary_location":{"id":"doi:10.1609/aaaiss.v9i1.42925","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaaiss.v9i1.42925","pdf_url":"https://ojs.aaai.org/index.php/AAAI-SS/article/download/42925/50485","source":{"id":"https://openalex.org/S4389157828","display_name":"Proceedings of the AAAI Symposium Series","issn_l":"2994-4317","issn":["2994-4317"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Symposium Series","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI-SS/article/download/42925/50485","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139130500","display_name":"Soyeon Park","orcid":null},"institutions":[{"id":"https://openalex.org/I113825674","display_name":"Handong Global University","ror":"https://ror.org/00txhkt32","country_code":"KR","type":"education","lineage":["https://openalex.org/I113825674"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soyeon Park","raw_affiliation_strings":["Handong Global University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Handong Global University","institution_ids":["https://openalex.org/I113825674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040188814","display_name":"Doohee Chung","orcid":"https://orcid.org/0000-0002-7413-0778"},"institutions":[{"id":"https://openalex.org/I113825674","display_name":"Handong Global University","ror":"https://ror.org/00txhkt32","country_code":"KR","type":"education","lineage":["https://openalex.org/I113825674"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Doohee Chung","raw_affiliation_strings":["Handong Global University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Handong Global University","institution_ids":["https://openalex.org/I113825674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5139190689","display_name":"Charmgil Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I113825674","display_name":"Handong Global University","ror":"https://ror.org/00txhkt32","country_code":"KR","type":"education","lineage":["https://openalex.org/I113825674"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Charmgil Hong","raw_affiliation_strings":["Handong Global University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Handong Global University","institution_ids":["https://openalex.org/I113825674"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I113825674"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76706272,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":"1","first_page":"195","last_page":"203"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.5329999923706055,"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":0.5329999923706055,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.11630000174045563,"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":0.093299999833107,"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/time-series","display_name":"Time series","score":0.478300005197525},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4699999988079071},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.46889999508857727},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4657999873161316},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.413100004196167},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.37869998812675476},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.366100013256073}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6371999979019165},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.478300005197525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47360000014305115},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4699999988079071},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4657999873161316},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.413100004196167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3815000057220459},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.37869998812675476},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.366100013256073},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.358599990606308},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.34540000557899475},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34119999408721924},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3330000042915344},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.32280001044273376},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C2779439359","wikidata":"https://www.wikidata.org/wiki/Q317088","display_name":"Commodity","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.26809999346733093}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaaiss.v9i1.42925","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaaiss.v9i1.42925","pdf_url":"https://ojs.aaai.org/index.php/AAAI-SS/article/download/42925/50485","source":{"id":"https://openalex.org/S4389157828","display_name":"Proceedings of the AAAI Symposium Series","issn_l":"2994-4317","issn":["2994-4317"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Symposium Series","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/42925","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI-SS/article/view/42925","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2994-4317","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaaiss.v9i1.42925","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaaiss.v9i1.42925","pdf_url":"https://ojs.aaai.org/index.php/AAAI-SS/article/download/42925/50485","source":{"id":"https://openalex.org/S4389157828","display_name":"Proceedings of the AAAI Symposium Series","issn_l":"2994-4317","issn":["2994-4317"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Symposium Series","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1616197624","display_name":null,"funder_award_id":"2023-0-00055","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7165638262.pdf","grobid_xml":"https://content.openalex.org/works/W7165638262.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Forecasting":[0],"multivariate":[1,78],"time":[2,39,140],"series":[3,40],"remains":[4],"challenging":[5],"due":[6],"to":[7,73,101],"complex":[8,138],"cross-variable":[9],"dependencies":[10,76],"and":[11,28,34,65,77,119,130],"the":[12,125],"presence":[13],"of":[14,127],"heterogeneous":[15],"external":[16],"influences.":[17],"This":[18],"paper":[19],"presents":[20],"Spectrogram-Enhanced":[21],"Multimodal":[22],"Fusion":[23],"(SEMF),":[24],"which":[25,48],"combines":[26],"spectral":[27],"temporal":[29,75],"representations":[30],"for":[31],"more":[32],"accurate":[33],"robust":[35],"forecasting.":[36],"The":[37],"target":[38],"is":[41],"transformed":[42],"into":[43,87],"Morlet":[44],"wavelet":[45],"spectrograms,":[46],"from":[47],"a":[49,71,88],"Vision":[50],"Transformer":[51,72],"encoder":[52],"extracts":[53],"localized,":[54],"frequency-aware":[55],"features.":[56],"In":[57],"parallel,":[58],"exogenous":[59],"variables,":[60],"such":[61],"as":[62],"financial":[63,139],"indicators":[64],"macroeconomic":[66],"signals,":[67],"are":[68],"encoded":[69],"via":[70],"capture":[74],"dynamics.":[79],"A":[80],"bidirectional":[81],"cross-attention":[82],"module":[83],"integrates":[84],"these":[85],"modalities":[86],"unified":[89],"representation":[90],"that":[91],"preserves":[92],"distinct":[93],"signal":[94],"characteristics":[95],"while":[96],"modeling":[97],"cross-modal":[98],"correlations.":[99],"Applied":[100],"multiple":[102,116],"commodity":[103],"price":[104],"forecasting":[105,117],"tasks,":[106],"SEMF":[107],"achieves":[108],"consistent":[109],"improvements":[110],"over":[111],"six":[112],"competitive":[113],"baselines":[114],"across":[115],"horizons":[118],"evaluation":[120],"metrics.":[121],"These":[122],"results":[123],"demonstrate":[124],"effectiveness":[126],"multimodal":[128],"fusion":[129],"spectrogram-based":[131],"encoding":[132],"in":[133],"capturing":[134],"multi-scale":[135],"patterns":[136],"within":[137],"series.":[141]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2026-06-24T00:00:00"}
