{"id":"https://openalex.org/W3203298641","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534351","title":"Temporal Convolutional Attention Neural Networks for Time Series Forecasting","display_name":"Temporal Convolutional Attention Neural Networks for Time Series Forecasting","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3203298641","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534351","mag":"3203298641"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534351","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5101505672","display_name":"Yang Lin","orcid":"https://orcid.org/0000-0003-4168-6750"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yang Lin","raw_affiliation_strings":["School of Computer Science, University of Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079360974","display_name":"Irena Koprinska","orcid":"https://orcid.org/0000-0001-9479-4187"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Irena Koprinska","raw_affiliation_strings":["School of Computer Science, University of Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068647269","display_name":"Mashud Rana","orcid":"https://orcid.org/0000-0003-2999-9367"},"institutions":[{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"government","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mashud Rana","raw_affiliation_strings":["Data61 CSIRO,Sydney,Australia"],"affiliations":[{"raw_affiliation_string":"Data61 CSIRO,Sydney,Australia","institution_ids":["https://openalex.org/I42894916","https://openalex.org/I1292875679"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.051,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.999914,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9996,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9996,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9982,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9801,"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/interpretability","display_name":"Interpretability","score":0.8858628},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4304915}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8858628},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7827679},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.75350773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6907683},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.63547873},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.54671264},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4666994},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44707787},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4304915},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.42718714},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4211861},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06457803},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534351","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.45}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":26,"referenced_works":["https://openalex.org/W2031939255","https://openalex.org/W2117671523","https://openalex.org/W2194775991","https://openalex.org/W2253795368","https://openalex.org/W2519091744","https://openalex.org/W2552480641","https://openalex.org/W2792764867","https://openalex.org/W2889928394","https://openalex.org/W2897257904","https://openalex.org/W2949382160","https://openalex.org/W2950858167","https://openalex.org/W2954731415","https://openalex.org/W2962788496","https://openalex.org/W2963123301","https://openalex.org/W2963403868","https://openalex.org/W2963840672","https://openalex.org/W2964308564","https://openalex.org/W2970631142","https://openalex.org/W2970777192","https://openalex.org/W2980994438","https://openalex.org/W2996552856","https://openalex.org/W3011590044","https://openalex.org/W3089349310","https://openalex.org/W3101467051","https://openalex.org/W4294102323","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W4361193272","https://openalex.org/W4312417841","https://openalex.org/W4310278675","https://openalex.org/W4226493464","https://openalex.org/W3193565141","https://openalex.org/W3167935049","https://openalex.org/W3133861977","https://openalex.org/W3029198973","https://openalex.org/W2905433371","https://openalex.org/W2806259446"],"abstract_inverted_index":{"Temporal":[0],"Convolutional":[1],"Neural":[2],"Networks":[3],"(TCNNs)":[4],"have":[5],"been":[6],"applied":[7],"for":[8,56,67,112,152,170],"various":[9],"sequence":[10,27],"modelling":[11],"tasks":[12],"including":[13,137],"time":[14],"series":[15],"forecasting.":[16,70],"However,":[17],"TCNNs":[18],"may":[19],"require":[20],"many":[21],"convolutional":[22,75,148],"layers":[23,149],"if":[24],"the":[25,73,91,115,166,171],"input":[26],"is":[28,157,162],"long":[29],"and":[30,59,83,110,161],"are":[31],"not":[32],"able":[33,163],"to":[34,79,88,159,164],"provide":[35],"interpretable":[36],"results.":[37,117],"In":[38],"this":[39],"paper,":[40],"we":[41],"present":[42],"TCAN,":[43],"a":[44,64,107],"novel":[45],"deep":[46,133],"learning":[47,134],"approach":[48],"that":[49,128],"employs":[50],"attention":[51,87,96],"mechanism":[52],"with":[53],"temporal":[54,81],"convolutions":[55],"probabilistic":[57],"forecasting,":[58],"demonstrate":[60],"its":[61],"performance":[62],"in":[63,139],"case":[65],"study":[66],"solar":[68,123],"power":[69,124],"TCAN":[71,99,129,143],"uses":[72,85],"hierarchical":[74],"structure":[76],"of":[77,98,114,141,147],"TCNN":[78,138,151],"extract":[80],"dependencies":[82],"then":[84],"sparse":[86,95],"focus":[89],"on":[90],"important":[92,168],"timesteps.":[93],"The":[94],"layer":[97],"enables":[100],"an":[101,153],"extended":[102,154],"receptive":[103,155],"field":[104],"without":[105],"requiring":[106],"deeper":[108],"architecture":[109],"allows":[111],"interpretability":[113],"forecasting":[116,135],"An":[118],"evaluation":[119],"using":[120],"three":[121],"large":[122],"data":[125],"sets":[126],"demonstrates":[127],"outperforms":[130],"several":[131],"state-of-the-art":[132],"models":[136],"terms":[140],"accuracy.":[142],"requires":[144],"less":[145],"number":[146],"than":[150],"field,":[156],"faster":[158],"train":[160],"visualize":[165],"most":[167],"timesteps":[169],"prediction.":[172]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3203298641","counts_by_year":[{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1}],"updated_date":"2025-01-02T20:03:13.013156","created_date":"2021-10-11"}