{"id":"https://openalex.org/W7124681531","doi":"https://doi.org/10.1186/s40537-025-01362-9","title":"Multi-view graph representing interactive learning network for time series forecasting","display_name":"Multi-view graph representing interactive learning network for time series forecasting","publication_year":2026,"publication_date":"2026-01-18","ids":{"openalex":"https://openalex.org/W7124681531","doi":"https://doi.org/10.1186/s40537-025-01362-9"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-025-01362-9","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01362-9","pdf_url":null,"source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1186/s40537-025-01362-9","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109657035","display_name":"Guanshu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Guanshu Wang","raw_affiliation_strings":["Shandong Institute of Petroleum and Chemical Technology, Dongying, 257061, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Institute of Petroleum and Chemical Technology, Dongying, 257061, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100388549","display_name":"Limin Liu","orcid":"https://orcid.org/0009-0004-0913-1127"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Limin Liu","raw_affiliation_strings":["Shandong Institute of Petroleum and Chemical Technology, Dongying, 257061, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Institute of Petroleum and Chemical Technology, Dongying, 257061, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123337613","display_name":"Bin Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bin Wu","raw_affiliation_strings":["Shandong Institute of Petroleum and Chemical Technology, Dongying, 257061, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Institute of Petroleum and Chemical Technology, Dongying, 257061, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5109657035"],"corresponding_institution_ids":[],"apc_list":{"value":1990,"currency":"USD","value_usd":1990},"apc_paid":{"value":1990,"currency":"USD","value_usd":1990},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04641315,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"1","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":0.6428999900817871,"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":0.6428999900817871,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.09430000185966492,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.08730000257492065,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5625},{"id":"https://openalex.org/keywords/interdependence","display_name":"Interdependence","score":0.5562000274658203},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4740000069141388},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.47040000557899475},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.37470000982284546},{"id":"https://openalex.org/keywords/time-horizon","display_name":"Time horizon","score":0.36890000104904175},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.3264999985694885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8409000039100647},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5625},{"id":"https://openalex.org/C185874996","wikidata":"https://www.wikidata.org/wiki/Q269699","display_name":"Interdependence","level":2,"score":0.5562000274658203},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4740000069141388},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.47040000557899475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46709999442100525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.453900009393692},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38989999890327454},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C28761237","wikidata":"https://www.wikidata.org/wiki/Q7805321","display_name":"Time horizon","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C2987015589","wikidata":"https://www.wikidata.org/wiki/Q1040098","display_name":"Learning network","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28119999170303345},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-025-01362-9","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01362-9","pdf_url":null,"source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:33656ad10d0a44c09c0a8534154d5bcb","is_oa":true,"landing_page_url":"https://doaj.org/article/33656ad10d0a44c09c0a8534154d5bcb","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 13, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-025-01362-9","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01362-9","pdf_url":null,"source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9155382513999939,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1964357740","https://openalex.org/W2003602843","https://openalex.org/W2131774270","https://openalex.org/W2194775991","https://openalex.org/W2565330852","https://openalex.org/W2604847698","https://openalex.org/W2756203131","https://openalex.org/W2962752580","https://openalex.org/W2965341826","https://openalex.org/W2980994438","https://openalex.org/W2996847713","https://openalex.org/W2997848713","https://openalex.org/W3019433526","https://openalex.org/W3080253043","https://openalex.org/W3123909522","https://openalex.org/W3126367810","https://openalex.org/W3175016653","https://openalex.org/W3177318507","https://openalex.org/W3180367960","https://openalex.org/W4283315029","https://openalex.org/W4283721567","https://openalex.org/W4293207828","https://openalex.org/W4382203079","https://openalex.org/W4382318973","https://openalex.org/W4391311137","https://openalex.org/W4403539871"],"related_works":[],"abstract_inverted_index":{"Time":[0],"series":[1],"forecasting":[2,50,166],"plays":[3],"a":[4,148],"vital":[5],"role":[6],"in":[7,45],"domains":[8],"such":[9],"as":[10],"energy":[11],"consumption":[12],"estimation,":[13],"weather":[14],"analysis,":[15],"and":[16,28,42,83,94,128,135,168],"traffic":[17],"flow":[18],"prediction,":[19],"where":[20],"accurate":[21],"predictions":[22],"are":[23],"crucial":[24],"for":[25],"effective":[26],"planning":[27],"decision-making.":[29],"However,":[30],"current":[31],"methods":[32],"often":[33],"struggle":[34],"to":[35,73,89,98,110],"model":[36,68,111],"complex":[37],"interdependencies":[38],"among":[39],"multiple":[40],"variables":[41],"localized":[43],"dynamics":[44],"long-range":[46],"sequences,":[47],"which":[48],"limits":[49],"performance.":[51],"To":[52],"overcome":[53],"these":[54],"limitations,":[55],"this":[56],"paper":[57],"introduces":[58],"the":[59,144,163],"Multi-view":[60],"Graph":[61],"Representing":[62],"Interactive":[63],"Learning":[64],"Network":[65],"(MGRILN).":[66],"The":[67],"leverages":[69],"graph":[70],"convolutional":[71],"networks":[72],"build":[74],"topological":[75],"structures":[76],"from":[77],"three":[78],"complementary":[79],"perspectives:":[80],"temporal,":[81],"dimensional,":[82],"cross-segmental.":[84],"It":[85],"uses":[86],"differential":[87],"operations":[88],"capture":[90],"inter-dimensional":[91],"trend":[92],"shifts":[93],"constructs":[95],"cross-segment":[96],"graphs":[97],"represent":[99],"local":[100],"volatility":[101],"correlations.":[102],"A":[103],"multi-scale":[104],"interaction":[105],"module":[106],"is":[107],"also":[108],"integrated":[109],"dependencies":[112],"across":[113],"different":[114],"temporal":[115],"resolutions.":[116],"Comprehensive":[117],"experiments":[118],"on":[119,143],"five":[120],"real-world":[121],"datasets":[122],"show":[123],"that":[124],"MGRILN":[125],"reduces":[126],"MAE":[127,157],"MSE":[129],"by":[130],"an":[131,156],"average":[132],"of":[133,151,158],"8.7%":[134],"9.2%,":[136],"respectively,":[137],"outperforming":[138],"state-of-the-art":[139],"benchmarks.":[140],"For":[141],"example,":[142],"Weather":[145],"dataset":[146],"with":[147],"prediction":[149],"horizon":[150],"720":[152],"steps,":[153],"it":[154],"achieves":[155],"0.334.":[159],"These":[160],"results":[161],"demonstrate":[162],"model\u2019s":[164],"strong":[165],"capability":[167],"practical":[169],"value.":[170]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2026-01-20T00:00:00"}
