{"id":"https://openalex.org/W4387448464","doi":"https://doi.org/10.1109/access.2023.3322957","title":"Enformer: Encoder-Based Sparse Periodic Self-Attention Time-Series Forecasting","display_name":"Enformer: Encoder-Based Sparse Periodic Self-Attention Time-Series Forecasting","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387448464","doi":"https://doi.org/10.1109/access.2023.3322957"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3322957","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3322957","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10274070.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10274070.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102763343","display_name":"Na Wang","orcid":"https://orcid.org/0009-0000-3380-6113"},"institutions":[{"id":"https://openalex.org/I206777745","display_name":"Nanjing Audit University","ror":"https://ror.org/04zj2bd87","country_code":"CN","type":"education","lineage":["https://openalex.org/I206777745"]},{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Na Wang","raw_affiliation_strings":["Department of Accounting and Audit, Nanjing Audit University Jinshen College, Nanjing, China","College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China","Department of Accounting and Audit, Nanjing Audit University Jinshen College, Nanjing 210046, China"],"raw_orcid":"https://orcid.org/0009-0000-3380-6113","affiliations":[{"raw_affiliation_string":"Department of Accounting and Audit, Nanjing Audit University Jinshen College, Nanjing, China","institution_ids":["https://openalex.org/I206777745"]},{"raw_affiliation_string":"College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China","institution_ids":["https://openalex.org/I9842412"]},{"raw_affiliation_string":"Department of Accounting and Audit, Nanjing Audit University Jinshen College, Nanjing 210046, China","institution_ids":["https://openalex.org/I206777745"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113786355","display_name":"Xianglian Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianglian Zhao","raw_affiliation_strings":["College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China","College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]},{"raw_affiliation_string":"College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.5657,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.84365156,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"112004","last_page":"112014"},"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9980999827384949,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9952999949455261,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8124731779098511},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5526790618896484},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5129098892211914},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.49997448921203613},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.45700323581695557},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40531104803085327},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3628265857696533},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3454107642173767},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.31156063079833984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8124731779098511},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5526790618896484},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5129098892211914},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.49997448921203613},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.45700323581695557},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40531104803085327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3628265857696533},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3454107642173767},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31156063079833984},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3322957","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3322957","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10274070.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:fc2098413c5c4084aee7eed45acce219","is_oa":true,"landing_page_url":"https://doaj.org/article/fc2098413c5c4084aee7eed45acce219","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":"IEEE Access, Vol 11, Pp 112004-112014 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3322957","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3322957","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10274070.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387448464.pdf","grobid_xml":"https://content.openalex.org/works/W4387448464.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1966676388","https://openalex.org/W2016287239","https://openalex.org/W2026653933","https://openalex.org/W2030933600","https://openalex.org/W2046245205","https://openalex.org/W2064675550","https://openalex.org/W2130942839","https://openalex.org/W2531361673","https://openalex.org/W2603648311","https://openalex.org/W2747599906","https://openalex.org/W2792764867","https://openalex.org/W2889928394","https://openalex.org/W2890096158","https://openalex.org/W2891929938","https://openalex.org/W2952278188","https://openalex.org/W2954731415","https://openalex.org/W2963532813","https://openalex.org/W2963951265","https://openalex.org/W2970309699","https://openalex.org/W2970777192","https://openalex.org/W3177318507","https://openalex.org/W3212890323","https://openalex.org/W4292779060","https://openalex.org/W4295838474","https://openalex.org/W4323654151","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6679436768","https://openalex.org/W6735826779","https://openalex.org/W6739901393","https://openalex.org/W6749825310","https://openalex.org/W6754779804","https://openalex.org/W6761628794","https://openalex.org/W6762250955","https://openalex.org/W6764679822","https://openalex.org/W6764770144","https://openalex.org/W6771626834","https://openalex.org/W6778883912","https://openalex.org/W6797155008"],"related_works":["https://openalex.org/W2931688134","https://openalex.org/W2377919138","https://openalex.org/W2378857091","https://openalex.org/W4256502920","https://openalex.org/W103652678","https://openalex.org/W2999756192","https://openalex.org/W4226090359","https://openalex.org/W4382701072","https://openalex.org/W2011624601","https://openalex.org/W4390451065"],"abstract_inverted_index":{"Time-series":[0],"forecasting":[1,67,100],"makes":[2],"reasonable":[3,252],"prediction":[4,56,239,259],"and":[5,14,27,237,266],"planning":[6],"on":[7,227],"stock":[8],"forecast,":[9],"traffic":[10],"flow,":[11],"power":[12],"consumption":[13],"extreme":[15],"weather":[16],"warning":[17],"through":[18],"the":[19,44,54,59,65,83,94,113,125,128,131,140,144,149,154,175,183,193,207,210,214,217,230,238,251,262,270,273],"correlation":[20],"characteristics":[21],"of":[22,62,87,98,116,143,178,195,209,216,233,254,264],"its":[23],"own":[24],"multi-dimensional":[25],"data":[26],"time.":[28,275],"The":[29,160,186],"proposed":[30,137,219],"Transformer":[31,63,109,118],"structure":[32,110,115,256],"in":[33,220,229,257],"natural":[34,88],"language":[35,89],"processing":[36,85,90,96],"is":[37,50,136,163,223,242,248],"proved":[38,249],"to":[39,123,138,174,192,206,269],"be":[40],"beneficial":[41],"for":[42,53,201],"establishing":[43],"global":[45],"dependence":[46,177],"between":[47],"vectors,":[48],"which":[49,170],"undoubtedly":[51],"advantageous":[52],"time":[55,158,202,240],"problem.":[57],"However,":[58],"unchanged":[60],"introduction":[61],"into":[64],"time-series":[66,99,258],"will":[68],"also":[69,164,243],"bring":[70],"corresponding":[71],"problems,":[72,91],"including":[73],"high":[74],"computing":[75],"complexity,":[76],"vector":[77],"information":[78],"redundancy.":[79],"And":[80],"bound":[81],"by":[82,225,245],"unique":[84],"methods":[86,97],"it":[92,198],"ignores":[93],"common":[95],"problems.":[101],"To":[102],"this":[103,221],"end,":[104],"we":[105],"design":[106],"a":[107],"lightweight":[108],"that":[111,148,250],"breaks":[112],"cascade":[114],"traditional":[117],"encoders-decoders,":[119],"only":[120],"using":[121],"encoder":[122],"build":[124],"network.":[126],"At":[127],"same":[129,274],"time,":[130],"CMM":[132],"(Coarse":[133],"Matching":[134],"Module)":[135],"construct":[139],"multi-scale":[141],"output":[142],"input":[145],"sequence,":[146],"so":[147],"subsequent":[150],"networks":[151],"can":[152,260],"capture":[153],"dependencies":[155],"with":[156],"different":[157],"granularity.":[159],"self-attention":[161],"head":[162],"redesigned,":[165],"called":[166],"sparse":[167],"periodic":[168,176],"attention,":[169],"pays":[171],"more":[172,199],"attention":[173],"sequence":[179],"context":[180],"while":[181],"reducing":[182],"computational":[184],"complexity.":[185],"overall":[187],"change":[188],"gives":[189],"full":[190],"play":[191],"advantages":[194],"Transformer,":[196],"making":[197],"suitable":[200],"series":[203],"prediction.":[204],"Thanks":[205],"synergy":[208],"above":[211],"innovative":[212],"work,":[213],"accuracy":[215,271],"algorithm":[218],"paper":[222],"improved":[224,244],"7.1%":[226],"average":[228],"testing":[231],"experiments":[232],"multiple":[234],"public":[235],"datasets,":[236],"efficiency":[241],"44.1%.":[246],"It":[247],"improvement":[253],"transformer":[255],"reduce":[261],"amount":[263],"calculation":[265],"give":[267],"consideration":[268],"at":[272]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}