{"id":"https://openalex.org/W4311995575","doi":"https://doi.org/10.1080/01969722.2022.2122010","title":"An Integrated Dual Attention with Convolutional LSTM for Short-Term Temperature Forecasting","display_name":"An Integrated Dual Attention with Convolutional LSTM for Short-Term Temperature Forecasting","publication_year":2022,"publication_date":"2022-12-09","ids":{"openalex":"https://openalex.org/W4311995575","doi":"https://doi.org/10.1080/01969722.2022.2122010"},"language":"en","primary_location":{"id":"doi:10.1080/01969722.2022.2122010","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2022.2122010","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-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/A5006709815","display_name":"Tham Vo","orcid":"https://orcid.org/0000-0001-7291-4168"},"institutions":[{"id":"https://openalex.org/I4210111957","display_name":"B\u00ecnh D\u01b0\u01a1ng University","ror":"https://ror.org/02b9zqw68","country_code":"VN","type":"education","lineage":["https://openalex.org/I4210111957"]},{"id":"https://openalex.org/I4391012539","display_name":"Thu Dau Mot University","ror":"https://ror.org/010y5b925","country_code":null,"type":"education","lineage":["https://openalex.org/I4391012539"]}],"countries":["VN"],"is_corresponding":true,"raw_author_name":"Tham Vo","raw_affiliation_strings":["Thu Dau Mot University, Binh Duong, Vietnam"],"raw_orcid":"https://orcid.org/0000-0001-7291-4168","affiliations":[{"raw_affiliation_string":"Thu Dau Mot University, Binh Duong, Vietnam","institution_ids":["https://openalex.org/I4210111957","https://openalex.org/I4391012539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5006709815"],"corresponding_institution_ids":["https://openalex.org/I4210111957","https://openalex.org/I4391012539"],"apc_list":null,"apc_paid":null,"fwci":0.779,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.67108646,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"55","issue":"2","first_page":"511","last_page":"533"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.994700014591217,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9945999979972839,"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":0.8253078460693359},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6558080315589905},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6548293828964233},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6363235116004944},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5992282032966614},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.5705159306526184},{"id":"https://openalex.org/keywords/chaotic","display_name":"Chaotic","score":0.5658388137817383},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5257725715637207},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5037974715232849},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.49680760502815247},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4788108468055725},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4503239691257477},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4165456295013428},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32054439187049866}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8253078460693359},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6558080315589905},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6548293828964233},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6363235116004944},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5992282032966614},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.5705159306526184},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.5658388137817383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5257725715637207},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5037974715232849},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.49680760502815247},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4788108468055725},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4503239691257477},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4165456295013428},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32054439187049866},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/01969722.2022.2122010","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2022.2122010","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320318527","display_name":"Tr\u01b0\u1eddng \u0110\u1ea1i H\u1ecdc Th\u1ee7 D\u1ea7u M\u1ed9t","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1083019413","https://openalex.org/W1902237438","https://openalex.org/W2064675550","https://openalex.org/W2094040832","https://openalex.org/W2133564696","https://openalex.org/W2559655401","https://openalex.org/W2612690371","https://openalex.org/W2809928955","https://openalex.org/W2909992207","https://openalex.org/W2951251697","https://openalex.org/W2989672005","https://openalex.org/W3010758682","https://openalex.org/W3012079496","https://openalex.org/W3042800119","https://openalex.org/W3048037402","https://openalex.org/W3096902305","https://openalex.org/W3109365969","https://openalex.org/W3133832801","https://openalex.org/W3137522621","https://openalex.org/W3185274067","https://openalex.org/W3202925140","https://openalex.org/W3206853280","https://openalex.org/W3213601681","https://openalex.org/W4200128945","https://openalex.org/W4224551291","https://openalex.org/W4226245930","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W3032952384","https://openalex.org/W3034302643","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W2964335273","https://openalex.org/W1889624880","https://openalex.org/W2229372569"],"abstract_inverted_index":{"In":[0],"recent":[1,102,173],"years,":[2],"there":[3,57],"are":[4,58],"multiple":[5],"temperature":[6,34,66,81,91,147],"predictive":[7,67],"models":[8],"have":[9],"demonstrated":[10,163],"the":[11,33,71,80,86,98,122,139,152,164],"effectiveness":[12,165],"and":[13,27,141,157],"outperformances":[14],"of":[15,39,62,73,101,166],"applying":[16],"different":[17],"deep":[18,64,103],"neural":[19,24,29],"architectures,":[20],"such":[21],"as":[22],"convolutional":[23],"network":[25,30],"(CNN)":[26],"recurrent":[28],"(RNN)":[31],"for":[32],"forecasting":[35],"task":[36],"in":[37,43,97,110,160,170],"forms":[38],"time-series":[40],"analysis":[41],"problem":[42],"comparing":[44,171],"with":[45,121,172],"previous":[46],"traditional":[47],"machine":[48],"learning":[49,83],"based":[50],"techniques.":[51,105],"However,":[52],"up":[53],"to":[54,70,95,136],"this":[55,111],"time,":[56],"still":[59],"several":[60],"limitations":[61],"existing":[63],"learning-based":[65,104],"methods":[68],"related":[69],"capability":[72],"efficiently":[74],"integrating":[75],"extra":[76],"information":[77,144],"resources":[78],"into":[79],"data":[82,92],"process.":[84],"Moreover,":[85],"high-noised/chaotic":[87],"fluctuations":[88],"within":[89],"daily":[90,146],"also":[93],"lead":[94],"downgrades":[96],"accuracy":[99],"performance":[100],"To":[106],"overcome":[107],"these":[108],"challenges,":[109],"paper,":[112],"we":[113],"proposed":[114,133,168],"a":[115],"novel":[116],"integrated":[117],"dual":[118],"attention":[119],"mechanism":[120],"Convolutional":[123],"Long":[124],"Short-Term":[125],"Memory":[126],"Network":[127],"(LSTM),":[128],"called":[129],"as:":[130],"DAttConvLSTM.":[131],"Our":[132],"DAttCovLSTM":[134],"supports":[135],"effectively":[137],"capture":[138],"chaotic":[140],"dynamic":[142],"temporal":[143],"from":[145],"data,":[148],"thus":[149],"significantly":[150],"improve":[151],"prediction":[153],"results.":[154],"Extensive":[155],"experiments":[156],"comparative":[158],"studies":[159],"real-world":[161],"datasets":[162],"our":[167],"model":[169],"state-of-the-art":[174],"baselines.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
