{"id":"https://openalex.org/W4417468149","doi":"https://doi.org/10.48550/arxiv.2511.20577","title":"MSTN: Fast and Efficient Multivariate Time Series Prediction Model","display_name":"MSTN: Fast and Efficient Multivariate Time Series Prediction Model","publication_year":2025,"publication_date":"2025-11-25","ids":{"openalex":"https://openalex.org/W4417468149","doi":"https://doi.org/10.48550/arxiv.2511.20577"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.20577","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.20577","pdf_url":"https://arxiv.org/pdf/2511.20577","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.20577","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117457164","display_name":"Sumit S. Shevtekar","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shevtekar, Sumit S","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5035408769","display_name":"Chandresh Kumar Maurya","orcid":"https://orcid.org/0000-0003-3519-600X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maurya, Chandresh K","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5117457164"],"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.4124999940395355,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.4124999940395355,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.1264999955892563,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.10100000351667404,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4966000020503998},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.4837999939918518},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4684000015258789},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4674000144004822},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.41260001063346863},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.4083000123500824},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.37560001015663147},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.362199991941452},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.35910001397132874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7421000003814697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5202000141143799},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4966000020503998},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.4837999939918518},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4684000015258789},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4674000144004822},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.41260001063346863},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.35910001397132874},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.337799996137619},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3280999958515167},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3190000057220459},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C2781089630","wikidata":"https://www.wikidata.org/wiki/Q21856745","display_name":"Realization (probability)","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C25016198","wikidata":"https://www.wikidata.org/wiki/Q781833","display_name":"Temporal logic","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.28610000014305115},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27730000019073486},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.20577","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.20577","pdf_url":"https://arxiv.org/pdf/2511.20577","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"},{"id":"doi:10.48550/arxiv.2511.20577","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.20577","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.20577","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.20577","pdf_url":"https://arxiv.org/pdf/2511.20577","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"},"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":{"Real-world":[0],"time":[1],"series":[2],"often":[3],"exhibit":[4],"strong":[5,190],"non-stationarity,":[6],"complex":[7],"nonlinear":[8],"dynamics,":[9],"and":[10,52,110,118,157,175,177,195,208],"behaviour":[11],"expressed":[12],"across":[13],"multiple":[14],"temporal":[15,50,133],"scales,":[16],"from":[17],"rapid":[18],"local":[19,93],"fluctuations":[20],"to":[21,55,121,130,137],"slow-evolving":[22],"long-range":[23,102],"trends.":[24],"However,":[25],"many":[26],"contemporary":[27],"architectures":[28],"impose":[29],"rigid,":[30],"fixed-scale":[31],"structural":[32],"priors":[33],"--":[34,46],"such":[35],"as":[36],"patch-based":[37],"tokenization,":[38],"predefined":[39],"receptive":[40],"fields,":[41],"or":[42,107],"frozen":[43],"backbone":[44],"encoders":[45],"which":[47],"can":[48],"over-regularize":[49],"dynamics":[51],"limit":[53],"adaptability":[54],"abrupt":[56],"high-magnitude":[57],"events.":[58],"To":[59],"handle":[60],"this,":[61],"we":[62],"introduce":[63],"the":[64,142],"\\emph{Multi-scale":[65],"Temporal":[66,77],"Network}":[67],"(MSTN),":[68],"a":[69,86,96,112],"hybrid":[70],"neural":[71],"architecture":[72],"grounded":[73],"in":[74],"an":[75],"\\emph{Early":[76],"Aggregation}":[78],"principle.":[79],"MSTN":[80,129,160,192],"integrates":[81],"three":[82],"complementary":[83],"components:":[84],"(i)":[85],"multi-scale":[87],"convolutional":[88],"encoder":[89],"that":[90,100],"captures":[91],"fine-grained":[92],"structure;":[94],"(ii)":[95],"sequence":[97],"modeling":[98],"module":[99],"learns":[101],"dependencies":[103],"through":[104],"either":[105],"recurrent":[106],"attention-based":[108],"mechanisms;":[109],"(iii)":[111],"self-gated":[113],"fusion":[114],"stage":[115],"incorporating":[116],"squeeze-excitation":[117],"multi-head":[119],"attention":[120],"dynamically":[122],"modulate":[123],"cross-scale":[124],"representations.":[125],"This":[126],"design":[127],"enables":[128],"flexibly":[131],"model":[132],"patterns":[134],"spanning":[135],"milliseconds":[136],"extended":[138],"horizons,":[139],"while":[140],"avoiding":[141],"computational":[143],"burden":[144],"typically":[145],"associated":[146],"with":[147],"long-context":[148],"models.":[149],"Across":[150],"extensive":[151],"benchmarks":[152],"covering":[153],"forecasting,":[154],"imputation,":[155],"classification,":[156],"cross-dataset":[158],"generalization,":[159],"consistently":[161],"delivers":[162],"state-of-the-art":[163],"performance,":[164,191],"outperforming":[165],"recent":[166],"leading":[167],"approaches":[168],"including":[169],"TIME-LLM,":[170],"HiMTM,":[171],"SOFTS,":[172],"LLM4TS,":[173],"TimesNet,":[174],"PatchTST,":[176],"establishing":[178],"new":[179],"best":[180],"results":[181],"on":[182,205],"24":[183],"out":[184],"of":[185],"32":[186],"datasets.":[187],"Despite":[188],"its":[189],"remains":[193],"lightweight":[194],"supports":[196],"fast":[197],"inference,":[198],"making":[199],"it":[200],"well":[201],"suited":[202],"for":[203],"deployment":[204],"edge":[206],"devices":[207],"resource-constrained":[209],"environments.":[210]},"counts_by_year":[],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-11-28T00:00:00"}