{"id":"https://openalex.org/W4290945834","doi":"https://doi.org/10.1145/3534678.3539234","title":"Learning to Rotate","display_name":"Learning to Rotate","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290945834","doi":"https://doi.org/10.1145/3534678.3539234"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539234","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539234","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5100646104","display_name":"Weiqi Chen","orcid":"https://orcid.org/0009-0007-9246-9402"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiqi Chen","raw_affiliation_strings":["DAMO Academy, Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"DAMO Academy, Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101506414","display_name":"Wenwei Wang","orcid":"https://orcid.org/0000-0003-2292-7141"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenwei Wang","raw_affiliation_strings":["DAMO Academy, Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"DAMO Academy, Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025205936","display_name":"Bingqing Peng","orcid":"https://orcid.org/0009-0003-7515-897X"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingqing Peng","raw_affiliation_strings":["DAMO Academy, Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"DAMO Academy, Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048346353","display_name":"Qingsong Wen","orcid":"https://orcid.org/0000-0003-4516-2524"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]},{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qingsong Wen","raw_affiliation_strings":["DAMO Academy, Alibaba Group, Bellevue, WA, USA"],"affiliations":[{"raw_affiliation_string":"DAMO Academy, Alibaba Group, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210095624","https://openalex.org/I4210108985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101890076","display_name":"Tian Zhou","orcid":"https://orcid.org/0000-0003-1789-5413"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Zhou","raw_affiliation_strings":["DAMO Academy, Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"DAMO Academy, Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068194653","display_name":"Liang Sun","orcid":"https://orcid.org/0000-0001-8407-2201"},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]},{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liang Sun","raw_affiliation_strings":["DAMO Academy, Alibaba Group, Bellevue, WA, USA"],"affiliations":[{"raw_affiliation_string":"DAMO Academy, Alibaba Group, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210095624","https://openalex.org/I4210108985"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100646104"],"corresponding_institution_ids":["https://openalex.org/I45928872"],"apc_list":null,"apc_paid":null,"fwci":7.0522,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.98142317,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"146","last_page":"156"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9995999932289124,"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":0.9995999932289124,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9865999817848206,"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":0.6836449503898621},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6319082379341125},{"id":"https://openalex.org/keywords/quaternion","display_name":"Quaternion","score":0.5427733659744263},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4981858730316162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4840066134929657},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.44427019357681274},{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.4218232333660126},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4145064949989319},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40997034311294556},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34435558319091797},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3409498333930969},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1776791512966156},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10989031195640564},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.09420269727706909}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6836449503898621},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6319082379341125},{"id":"https://openalex.org/C200127275","wikidata":"https://www.wikidata.org/wiki/Q173853","display_name":"Quaternion","level":2,"score":0.5427733659744263},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4981858730316162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4840066134929657},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.44427019357681274},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.4218232333660126},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4145064949989319},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40997034311294556},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34435558319091797},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3409498333930969},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1776791512966156},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10989031195640564},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.09420269727706909},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539234","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539234","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1536447791","https://openalex.org/W1583837637","https://openalex.org/W2109316012","https://openalex.org/W2112828793","https://openalex.org/W2604847698","https://openalex.org/W2747599906","https://openalex.org/W2811507150","https://openalex.org/W2962752580","https://openalex.org/W2980994438","https://openalex.org/W3007098654","https://openalex.org/W3012775393","https://openalex.org/W3080157892","https://openalex.org/W3170140111","https://openalex.org/W4212774754","https://openalex.org/W4285176417"],"related_works":["https://openalex.org/W3127350447","https://openalex.org/W3108700312","https://openalex.org/W4225472102","https://openalex.org/W2382948573","https://openalex.org/W2887325221","https://openalex.org/W2084651007","https://openalex.org/W2184550115","https://openalex.org/W3121191907","https://openalex.org/W1984888409","https://openalex.org/W4308538550"],"abstract_inverted_index":{"Time":[0],"series":[1,19,33,117,154],"forecasting":[2,20],"is":[3],"a":[4],"critical":[5],"and":[6,57,103,156,161],"challenging":[7],"problem":[8],"in":[9,17,25,60,119],"many":[10],"real":[11],"applications.":[12],"Recently,":[13],"Transformer-based":[14],"models":[15,31,45],"prevail":[16],"time":[18,153],"due":[21],"to":[22,35,106,114,138,163],"their":[23],"advancement":[24],"long-range":[26],"dependencies":[27],"learning.":[28],"Besides,":[29],"some":[30],"introduce":[32],"decomposition":[34],"further":[36],"unveil":[37],"reliable":[38],"yet":[39],"plain":[40],"temporal":[41],"dependencies.":[42],"Unfortunately,":[43],"few":[44],"could":[46],"handle":[47],"complicated":[48],"periodical":[49,109],"patterns,":[50],"such":[51],"as":[52],"multiple":[53,151],"periods,":[54,56],"variable":[55],"phase":[58,104],"shifts":[59],"real-world":[61,152],"datasets.":[62],"Meanwhile,":[63],"the":[64,116,123,126,168],"notorious":[65],"quadratic":[66],"complexity":[67,141],"of":[68,122,130],"dot-product":[69],"attentions":[70],"hampers":[71],"long":[72],"sequence":[73],"modeling.":[74],"To":[75],"address":[76],"these":[77],"challenges,":[78],"we":[79],"design":[80],"an":[81,158],"innovative":[82],"framework":[83,149],"Quaternion":[84],"Transformer":[85],"(Quatformer),":[86],"along":[87],"with":[88],"three":[89],"major":[90],"components:":[91],"1).":[92],"learning-to-rotate":[93],"attention":[94],"(LRA)":[95],"based":[96],"on":[97,150],"quaternions":[98],"which":[99],"introduces":[100],"learnable":[101],"period":[102],"information":[105],"depict":[107],"intricate":[108],"patterns.":[110],"2).":[111],"trend":[112],"normalization":[113],"normalize":[115],"representations":[118],"hidden":[120],"layers":[121],"model":[124],"considering":[125],"slowly":[127],"varying":[128],"characteristic":[129],"trend.":[131],"3).":[132],"decoupling":[133],"LRA":[134],"using":[135],"global":[136],"memory":[137],"achieve":[139],"linear":[140],"without":[142],"losing":[143],"prediction":[144],"accuracy.":[145],"We":[146],"evaluate":[147],"our":[148],"datasets":[155],"observe":[157],"average":[159],"8.1%":[160],"up":[162],"18.5%":[164],"MSE":[165],"improvement":[166],"over":[167],"best":[169],"state-of-the-art":[170],"baseline.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2022-08-13T00:00:00"}