{"id":"https://openalex.org/W4321089625","doi":"https://doi.org/10.1145/3584704","title":"Fitting Imbalanced Uncertainties in Multi-output Time Series Forecasting","display_name":"Fitting Imbalanced Uncertainties in Multi-output Time Series Forecasting","publication_year":2023,"publication_date":"2023-02-16","ids":{"openalex":"https://openalex.org/W4321089625","doi":"https://doi.org/10.1145/3584704"},"language":"en","primary_location":{"id":"doi:10.1145/3584704","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3584704","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","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/A5050572857","display_name":"Jiezhu Cheng","orcid":"https://orcid.org/0000-0002-1755-6828"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiezhu Cheng","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong Province, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026022035","display_name":"Kaizhu Huang","orcid":"https://orcid.org/0000-0002-3034-9639"},"institutions":[{"id":"https://openalex.org/I4210159968","display_name":"Duke Kunshan University","ror":"https://ror.org/04sr5ys16","country_code":"CN","type":"education","lineage":["https://openalex.org/I170897317","https://openalex.org/I37461747","https://openalex.org/I4210159968"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaizhu Huang","raw_affiliation_strings":["Data Science Research Center, Duke Kunshan University, Suzhou, Jiangsu Province, China"],"affiliations":[{"raw_affiliation_string":"Data Science Research Center, Duke Kunshan University, Suzhou, Jiangsu Province, China","institution_ids":["https://openalex.org/I4210159968"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000582109","display_name":"Zibin Zheng","orcid":"https://orcid.org/0000-0002-7878-4330"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zibin Zheng","raw_affiliation_strings":["School of Software Engineering, Sun Yat-sen University, Zhuhai, Guangdong Province, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, Sun Yat-sen University, Zhuhai, Guangdong Province, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5050572857"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.8741,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.77459509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"17","issue":"7","first_page":"1","last_page":"23"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9968000054359436,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9968000054359436,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9966999888420105,"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":0.9959999918937683,"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/mixture-model","display_name":"Mixture model","score":0.7025890350341797},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6972861289978027},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5755852460861206},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5550609827041626},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5196275115013123},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4941845238208771},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4874127209186554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48236483335494995},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4644579589366913},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.46367383003234863},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4470231831073761},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.43716683983802795},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3475509583950043},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34013789892196655},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3312050998210907},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24581393599510193},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23314067721366882},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.09191066026687622}],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7025890350341797},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6972861289978027},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5755852460861206},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5550609827041626},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5196275115013123},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4941845238208771},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4874127209186554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48236483335494995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4644579589366913},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46367383003234863},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4470231831073761},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.43716683983802795},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3475509583950043},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34013789892196655},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3312050998210907},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24581393599510193},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23314067721366882},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.09191066026687622},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3584704","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3584704","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1378608539","display_name":null,"funder_award_id":"62032025","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W143057633","https://openalex.org/W1558606374","https://openalex.org/W1583837637","https://openalex.org/W1969852690","https://openalex.org/W1991151011","https://openalex.org/W2014928429","https://openalex.org/W2017377889","https://openalex.org/W2028072219","https://openalex.org/W2038381734","https://openalex.org/W2064675550","https://openalex.org/W2106742347","https://openalex.org/W2167464405","https://openalex.org/W2267186426","https://openalex.org/W2559997609","https://openalex.org/W2593294478","https://openalex.org/W2602856279","https://openalex.org/W2613328025","https://openalex.org/W2773625660","https://openalex.org/W2795953264","https://openalex.org/W2803192575","https://openalex.org/W2808800115","https://openalex.org/W2897713187","https://openalex.org/W2913340405","https://openalex.org/W2929703911","https://openalex.org/W2949468773","https://openalex.org/W2953231487","https://openalex.org/W2963122061","https://openalex.org/W2963677766","https://openalex.org/W2980994438","https://openalex.org/W2998367712","https://openalex.org/W3010198599","https://openalex.org/W3022643593","https://openalex.org/W3028305817","https://openalex.org/W3132782787","https://openalex.org/W3152893301","https://openalex.org/W3176196442","https://openalex.org/W3177318507","https://openalex.org/W3185846556","https://openalex.org/W3201637219","https://openalex.org/W4238202755"],"related_works":["https://openalex.org/W2473373438","https://openalex.org/W2368486525","https://openalex.org/W2077224612","https://openalex.org/W2153481672","https://openalex.org/W2153238387","https://openalex.org/W4312864369","https://openalex.org/W84255947","https://openalex.org/W2014842417","https://openalex.org/W2502114474","https://openalex.org/W2061347451"],"abstract_inverted_index":{"We":[0],"focus":[1],"on":[2,110,160],"multi-step":[3],"ahead":[4],"time":[5,29,94],"series":[6,95],"forecasting":[7,44,142],"with":[8,177],"the":[9,13,34,53,56,60,100,107,111,116,124,138,141,145,154,165,178,185],"multi-output":[10,43,93],"strategy.":[11],"From":[12],"perspective":[14],"of":[15,26,98,140,156,167],"multi-task":[16],"learning":[17],"(MTL),":[18],"we":[19,67,105],"recognize":[20],"imbalanced":[21,90],"uncertainties":[22,91],"between":[23,55,118],"prediction":[24,70,120],"tasks":[25,121],"different":[27,119],"future":[28],"steps.":[30],"Unexpectedly,":[31],"trained":[32,176],"by":[33],"standard":[35,179],"summed":[36,180],"Mean":[37],"Squared":[38],"Error":[39],"(MSE)":[40],"loss,":[41],"existing":[42],"models":[45,175],"may":[46],"suffer":[47],"from":[48],"performance":[49],"drops":[50],"due":[51],"to":[52,87,114,174],"inconsistency":[54],"loss":[57,85,172,182],"function":[58,86],"and":[59,79,122,144,188],"imbalance":[61],"structure.":[62],"To":[63],"address":[64],"this":[65,132],"problem,":[66],"reformulate":[68],"each":[69,128],"task":[71],"as":[72],"a":[73,81],"distinct":[74],"Gaussian":[75,83,170],"Mixture":[76],"Model":[77],"(GMM)":[78],"derive":[80],"multi-level":[82,169],"mixture":[84,146,171],"better":[88],"fit":[89],"in":[92,149],"forecasting.":[96],"Instead":[97],"using":[99],"two-step":[101,157],"Expectation-Maximization":[102],"(EM)":[103],"algorithm,":[104],"apply":[106],"self-attention":[108],"mechanism":[109],"task-specific":[112],"parameters":[113,139],"learn":[115],"correlations":[117],"generate":[123],"weight":[125],"distribution":[126],"for":[127],"GMM":[129],"component.":[130],"In":[131],"way,":[133],"our":[134,168],"method":[135],"jointly":[136],"optimizes":[137],"model":[143,147],"simultaneously":[148],"an":[150],"end-to-end":[151],"fashion,":[152],"avoiding":[153],"need":[155],"optimization.":[158],"Experiments":[159],"three":[161],"real-world":[162],"datasets":[163],"demonstrate":[164],"effectiveness":[166],"compared":[173],"MSE":[181],"function.":[183],"All":[184],"experimental":[186],"data":[187],"source":[189],"code":[190],"are":[191],"available":[192],"at":[193],"https://github.com/smallGum/GMM-FNN":[194],".":[195]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}