{"id":"https://openalex.org/W7168253644","doi":"https://doi.org/10.48550/arxiv.2607.10740","title":"Multi-Scale Convolution with Optimal Transport Attention Effect on Multivariate Time Series","display_name":"Multi-Scale Convolution with Optimal Transport Attention Effect on Multivariate Time Series","publication_year":2026,"publication_date":"2026-07-12","ids":{"openalex":"https://openalex.org/W7168253644","doi":"https://doi.org/10.48550/arxiv.2607.10740"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.10740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10740","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.10740","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140649586","display_name":"HaoChong Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, HaoChong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140619262","display_name":"Jian Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.5690000057220459,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.5690000057220459,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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":0.1776999980211258,"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":0.09040000289678574,"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/convolution","display_name":"Convolution (computer science)","score":0.696399986743927},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5982000231742859},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5224999785423279},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.47929999232292175},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4471000134944916},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.4307999908924103},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4221999943256378},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4036000072956085},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3930000066757202}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.696399986743927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6525999903678894},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5982000231742859},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5400999784469604},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5224999785423279},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.47929999232292175},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4652000069618225},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4471000134944916},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3930000066757202},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.36039999127388},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28349998593330383},{"id":"https://openalex.org/C29406490","wikidata":"https://www.wikidata.org/wiki/Q1420659","display_name":"Fisher information","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.27720001339912415},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.25769999623298645},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.10740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10740","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.10740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.10740","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,69],"analysis":[1],"of":[2,14,90,194],"Multivariate":[3],"Time":[4],"Series":[5],"(MTS)":[6],"plays":[7],"an":[8,132],"important":[9],"role":[10],"in":[11,43,113,181],"a":[12,48,77],"lot":[13],"real-world":[15],"practical":[16],"applications,":[17],"but":[18],"it":[19],"still":[20],"remains":[21],"some":[22],"challenging":[23],"problem":[24,135],"about":[25],"capturing":[26,109],"multi-granularity":[27],"structural":[28,111],"patterns":[29,112],"and":[30,79,136,161,173,184,198],"suppressing":[31],"noise":[32],"appropriately.":[33],"Multi-Scale":[34,94],"Convolution":[35,95],"with":[36,60],"Optimal":[37,124],"Transport":[38,125],"Attention":[39],"(MSC-OT)":[40],"is":[41,47],"proposed":[42,196],"this":[44],"paper.":[45],"MSC-OT":[46,88,177],"useful":[49],"architecture":[50],"to":[51,83,101,141,155,202],"optimize":[52],"the":[53,81,114,192],"attention":[54,102,129],"mechanism.":[55],"It":[56],"combines":[57],"multi-scale":[58,99],"convolution":[59],"Sinkhorn":[61,123],"optimal":[62,133],"transport":[63,134],"method":[64],"based":[65,105],"on":[66,106,165],"inverted":[67,70,107],"embedding.":[68],"embedding":[71],"approach":[72],"embeds":[73],"each":[74,195],"variable":[75],"as":[76,131],"token":[78],"allows":[80],"model":[82],"capture":[84],"cross-variate":[85],"relationships":[86],"better.":[87],"consists":[89],"two":[91],"part:":[92],"(1)":[93],"Enhancement,":[96],"that":[97,127,176],"applies":[98],"convolutions":[100],"score":[103],"matrices":[104],"embedding,":[108],"local":[110],"variate-interaction":[115],"space":[116],"induced":[117],"by":[118],"compressed":[119],"temporal":[120],"representations;":[121],"(2)":[122],"Regularization,":[126],"formulates":[128],"computation":[130],"employs":[137],"iterative":[138],"matrix":[139],"scaling":[140],"ensure":[142],"balanced":[143],"information":[144],"flow":[145],"across":[146],"variates.":[147],"Adaptive":[148],"Fusion":[149],"Strategy":[150],"utilizes":[151],"softmax-normalized":[152],"learnable":[153],"weights":[154],"dynamically":[156],"combine":[157],"base":[158],"attention,":[159],"convolution-enhanced,":[160],"OT-regularized":[162],"scores.":[163],"Experiments":[164],"widely-used":[166],"datasets,":[167],"including":[168],"ETT,":[169],"Electricity,":[170],"Traffic,":[171],"Solar-Energy,":[172],"Exchange-Rate,":[174],"show":[175],"achieves":[178],"well":[179],"performance":[180],"both":[182],"short-term":[183],"long-term":[185],"forecasting":[186],"tasks.":[187],"Ablation":[188],"experiments":[189],"further":[190],"validate":[191],"effectiveness":[193],"component":[197],"their":[199],"synergistic":[200],"contributions":[201],"improving":[203],"prediction":[204],"accuracy":[205],"for":[206],"multivariate":[207],"time":[208],"series":[209],"forecasting.":[210]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-15T00:00:00"}