{"id":"https://openalex.org/W3200748559","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533598","title":"Multiplicative Attention Mechanism for Multi-horizon Time Series Forecasting","display_name":"Multiplicative Attention Mechanism for Multi-horizon Time Series Forecasting","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200748559","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533598","mag":"3200748559"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068141067","display_name":"Runpeng Cui","orcid":"https://orcid.org/0000-0002-4737-788X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Runpeng Cui","raw_affiliation_strings":["State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100436368","display_name":"Jianqiang Wang","orcid":"https://orcid.org/0000-0003-4363-6108"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianqiang Wang","raw_affiliation_strings":["State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102853136","display_name":"Zheng Wang","orcid":"https://orcid.org/0000-0002-5596-9375"},"institutions":[{"id":"https://openalex.org/I4401726870","display_name":"Didi Chuxing (China)","ror":"https://ror.org/02ksqcf75","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726870"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["AI Labs, Didi Chuxing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Labs, Didi Chuxing, Beijing, China","institution_ids":["https://openalex.org/I4401726870"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068141067"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.7618,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.71244084,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9993000030517578,"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":0.9993000030517578,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9976000189781189,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9965000152587891,"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":0.779600977897644},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6830337047576904},{"id":"https://openalex.org/keywords/multiplicative-function","display_name":"Multiplicative function","score":0.6459903717041016},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6219385862350464},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.5514017343521118},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5324232578277588},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5128912925720215},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4897608757019043},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4799940288066864},{"id":"https://openalex.org/keywords/time-horizon","display_name":"Time horizon","score":0.4544459283351898},{"id":"https://openalex.org/keywords/horizon","display_name":"Horizon","score":0.4360300302505493},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.191261887550354},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09435340762138367}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.779600977897644},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6830337047576904},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.6459903717041016},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6219385862350464},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.5514017343521118},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5324232578277588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5128912925720215},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4897608757019043},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4799940288066864},{"id":"https://openalex.org/C28761237","wikidata":"https://www.wikidata.org/wiki/Q7805321","display_name":"Time horizon","level":2,"score":0.4544459283351898},{"id":"https://openalex.org/C159176650","wikidata":"https://www.wikidata.org/wiki/Q43261","display_name":"Horizon","level":2,"score":0.4360300302505493},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.191261887550354},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09435340762138367},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W196214544","https://openalex.org/W1498436455","https://openalex.org/W1522301498","https://openalex.org/W1810943226","https://openalex.org/W2017377889","https://openalex.org/W2064675550","https://openalex.org/W2085866051","https://openalex.org/W2117829824","https://openalex.org/W2133564696","https://openalex.org/W2167036165","https://openalex.org/W2510538700","https://openalex.org/W2525246036","https://openalex.org/W2552480641","https://openalex.org/W2604847698","https://openalex.org/W2747599906","https://openalex.org/W2766673981","https://openalex.org/W2773625660","https://openalex.org/W2797846142","https://openalex.org/W2889928394","https://openalex.org/W2949468773","https://openalex.org/W2954731415","https://openalex.org/W2963403868","https://openalex.org/W2964019776","https://openalex.org/W2964121744","https://openalex.org/W2964308564","https://openalex.org/W2969855422","https://openalex.org/W2970631142","https://openalex.org/W2970891497","https://openalex.org/W2970971581","https://openalex.org/W2980994438","https://openalex.org/W2996331899","https://openalex.org/W2996552856","https://openalex.org/W3044893918","https://openalex.org/W3080253043","https://openalex.org/W3080371803","https://openalex.org/W3089687835","https://openalex.org/W3123760665","https://openalex.org/W4241653265","https://openalex.org/W4287992009","https://openalex.org/W4295312788","https://openalex.org/W4385245566","https://openalex.org/W6607974698","https://openalex.org/W6631190155","https://openalex.org/W6638273328","https://openalex.org/W6679434410","https://openalex.org/W6727057065","https://openalex.org/W6729542563","https://openalex.org/W6739901393","https://openalex.org/W6746729860","https://openalex.org/W6750513420","https://openalex.org/W6754779804","https://openalex.org/W6763309814","https://openalex.org/W6764679822","https://openalex.org/W6766978945","https://openalex.org/W6767279920","https://openalex.org/W6771603238"],"related_works":["https://openalex.org/W2906267174","https://openalex.org/W134501823","https://openalex.org/W2615656344","https://openalex.org/W2013239059","https://openalex.org/W2295626654","https://openalex.org/W4286622840","https://openalex.org/W2536121351","https://openalex.org/W2809608782","https://openalex.org/W2333245620","https://openalex.org/W1889576908"],"abstract_inverted_index":{"Multi-horizon":[0],"time":[1,23,61,96,111],"series":[2,24],"forecasting":[3,37,76],"plays":[4],"an":[5],"important":[6],"role":[7],"in":[8,29],"many":[9],"industrial":[10],"and":[11,18],"business":[12],"decision":[13],"processes.":[14],"To":[15],"grasp":[16,106],"complex":[17,55],"various":[19],"patterns":[20,108],"across":[21,109],"different":[22,107],"is":[25],"the":[26,54,85],"crucial":[27],"step":[28],"achieving":[30],"promising":[31],"performance.":[32],"However,":[33],"most":[34],"deep":[35],"learning-based":[36],"approaches":[38],"simply":[39],"take":[40],"series-specific":[41,86],"static":[42],"(i.e.":[43],"time-invariant)":[44],"covariates":[45,87],"as":[46],"input":[47],"features,":[48],"which":[49],"can":[50],"fail":[51],"to":[52,73,80,88,101,105],"capture":[53],"pattern":[56],"variation":[57],"for":[58,93],"each":[59,94],"possible":[60,95],"series.":[62,97,112],"In":[63],"this":[64],"paper,":[65],"we":[66],"propose":[67],"a":[68,123],"novel":[69],"multiplicative":[70],"attention-based":[71],"architecture":[72],"tackle":[74],"such":[75],"problem.":[77],"Our":[78],"modification":[79],"multi-head":[81],"attention":[82,91],"layers":[83],"leverages":[84],"build":[89],"flexible":[90],"functions":[92],"This":[98],"improvement":[99],"contributes":[100],"greater":[102],"representation":[103],"capacity":[104],"related":[110],"Experiment":[113],"results":[114],"demonstrate":[115],"that":[116],"our":[117],"approach":[118],"achieves":[119],"state-of-the-art":[120],"performance":[121],"on":[122],"variety":[124],"of":[125],"real-world":[126],"datasets.":[127]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}