{"id":"https://openalex.org/W4408060656","doi":"https://doi.org/10.14778/3705829.3705842","title":"A Memory Guided Transformer for Time Series Forecasting","display_name":"A Memory Guided Transformer for Time Series Forecasting","publication_year":2024,"publication_date":"2024-10-01","ids":{"openalex":"https://openalex.org/W4408060656","doi":"https://doi.org/10.14778/3705829.3705842"},"language":"en","primary_location":{"id":"doi:10.14778/3705829.3705842","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3705829.3705842","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://vbn.aau.dk/ws/files/807944756/3705829.3705842.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074790294","display_name":"Yunyao Cheng","orcid":"https://orcid.org/0000-0002-1819-4056"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Yunyao Cheng","raw_affiliation_strings":["Aalborg University"],"affiliations":[{"raw_affiliation_string":"Aalborg University","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084021933","display_name":"Chenjuan Guo","orcid":"https://orcid.org/0000-0002-4516-4637"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenjuan Guo","raw_affiliation_strings":["East China Normal University"],"affiliations":[{"raw_affiliation_string":"East China Normal University","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072309548","display_name":"Bin Yang","orcid":"https://orcid.org/0000-0002-1658-1079"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Yang","raw_affiliation_strings":["East China Normal University"],"affiliations":[{"raw_affiliation_string":"East China Normal University","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004395799","display_name":"Haomin Yu","orcid":"https://orcid.org/0000-0003-1350-4796"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Haomin Yu","raw_affiliation_strings":["Aalborg University"],"affiliations":[{"raw_affiliation_string":"Aalborg University","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012251323","display_name":"Kai Zhao","orcid":"https://orcid.org/0000-0002-5159-2312"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Kai Zhao","raw_affiliation_strings":["Aalborg University"],"affiliations":[{"raw_affiliation_string":"Aalborg University","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029380368","display_name":"Christian S. Jensen","orcid":"https://orcid.org/0000-0002-9697-7670"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Christian S. Jensen","raw_affiliation_strings":["Aalborg University"],"affiliations":[{"raw_affiliation_string":"Aalborg University","institution_ids":["https://openalex.org/I891191580"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5074790294"],"corresponding_institution_ids":["https://openalex.org/I891191580"],"apc_list":null,"apc_paid":null,"fwci":0.6831,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71693514,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"18","issue":"2","first_page":"239","last_page":"252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"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":0.9998000264167786,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9984999895095825,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9944999814033508,"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/computer-science","display_name":"Computer science","score":0.7022688984870911},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6206902861595154},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.49312159419059753},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4657377302646637},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4530071020126343},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4239948093891144},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.42367714643478394},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4182102680206299},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23716765642166138},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.10870233178138733},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08811691403388977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7022688984870911},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6206902861595154},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.49312159419059753},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4657377302646637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4530071020126343},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4239948093891144},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.42367714643478394},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4182102680206299},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23716765642166138},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.10870233178138733},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08811691403388977},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14778/3705829.3705842","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3705829.3705842","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:publications/84fb6c7c-863f-42b4-af64-21bba166b18f","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/84fb6c7c-863f-42b4-af64-21bba166b18f","pdf_url":"https://vbn.aau.dk/ws/files/807944756/3705829.3705842.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Cheng, Y, Guo, C, Yang, B, Yu, H, Zhao, K & Jensen, C S 2024, 'A Memory Guided Transformer for Time Series Forecasting', Proceedings of the VLDB Endowment, vol. 18, no. 2, pp. 239-252. https://doi.org/10.14778/3705829.3705842","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/84fb6c7c-863f-42b4-af64-21bba166b18f","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/84fb6c7c-863f-42b4-af64-21bba166b18f","pdf_url":"https://vbn.aau.dk/ws/files/807944756/3705829.3705842.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Cheng, Y, Guo, C, Yang, B, Yu, H, Zhao, K & Jensen, C S 2024, 'A Memory Guided Transformer for Time Series Forecasting', Proceedings of the VLDB Endowment, vol. 18, no. 2, pp. 239-252. https://doi.org/10.14778/3705829.3705842","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4782604025","display_name":null,"funder_award_id":"9142-00001B","funder_id":"https://openalex.org/F4320313796","funder_display_name":"Innovationsfonden"}],"funders":[{"id":"https://openalex.org/F4320310490","display_name":"Villum Fonden","ror":"https://ror.org/007ww2d15"},{"id":"https://openalex.org/F4320313796","display_name":"Innovationsfonden","ror":"https://ror.org/00daj4111"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4408060656.pdf","grobid_xml":"https://content.openalex.org/works/W4408060656.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W2525579820","https://openalex.org/W2965341826","https://openalex.org/W3037671731","https://openalex.org/W3038981236","https://openalex.org/W3115964123","https://openalex.org/W3173539742","https://openalex.org/W3212890323","https://openalex.org/W4306316959","https://openalex.org/W4382449675","https://openalex.org/W4387561305","https://openalex.org/W4392453192","https://openalex.org/W4392453352","https://openalex.org/W4401126726","https://openalex.org/W4401353384","https://openalex.org/W4402042927"],"related_works":["https://openalex.org/W2770593030","https://openalex.org/W3154990682","https://openalex.org/W2560201613","https://openalex.org/W2171975302","https://openalex.org/W2022352247","https://openalex.org/W2488129135","https://openalex.org/W4312219546","https://openalex.org/W2787993192","https://openalex.org/W2377538627","https://openalex.org/W2107220315"],"abstract_inverted_index":{"Accurate":[0],"long-term":[1],"forecasting":[2,154],"from":[3],"multivariate":[4],"time":[5,21],"series":[6,22],"has":[7],"important":[8],"real-world":[9,157],"applications.":[10],"However,":[11],"achieving":[12],"this":[13],"so":[14],"is":[15,147],"challenging.":[16],"Thus,":[17],"analyses":[18],"reveal":[19],"that":[20,23,120,143],"span":[24],"long":[25],"durations":[26],"often":[27,44],"exhibit":[28],"dynamic":[29,40,89,131],"and":[30,59,63,81,91,113,133],"disrupted":[31,50,93,137],"correlations.":[32,138],"State-of-the-art":[33],"methods":[34],"employ":[35],"attention":[36],"mechanisms":[37],"to":[38,87,108,136],"capture":[39,88,109],"correlations,":[41,51],"but":[42],"they":[43],"do":[45],"not":[46],"contend":[47],"well":[48],"with":[49],"which":[52],"reduces":[53],"prediction":[54],"accuracy.":[55],"We":[56,97,116],"introduce":[57],"local":[58,112],"global":[60,82,114],"information":[61],"concepts":[62],"then":[64],"leverage":[65],"these":[66],"in":[67,153],"a":[68,100],"Memory":[69,103],"Guided":[70],"Transformer,":[71],"called":[72],"the":[73,84,106,124,127,144,151],"Memformer.":[74],"By":[75],"integrating":[76],"patch-wise":[77],"recurrent":[78],"graph":[79],"learning":[80],"attention,":[83],"Memformer":[85,107,128],"aims":[86],"correlations":[90,94,110,132],"take":[92],"into":[95,105,123],"account.":[96],"also":[98],"integrate":[99],"so-called":[101],"Alternating":[102],"Enhancer":[104],"between":[111],"information.":[115],"report":[117],"on":[118,156],"experiments":[119,140],"offer":[121,141],"insight":[122],"effectiveness":[125],"of":[126,149],"at":[129],"capturing":[130],"its":[134],"robustness":[135],"The":[139],"evidence":[142],"new":[145],"method":[146],"capable":[148],"advancing":[150],"state-of-the-art":[152],"accuracy":[155],"datasets.":[158]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}