{"id":"https://openalex.org/W2031817829","doi":"https://doi.org/10.1145/2487575.2487655","title":"A time-dependent enhanced support vector machine for time series regression","display_name":"A time-dependent enhanced support vector machine for time series regression","publication_year":2013,"publication_date":"2013-08-11","ids":{"openalex":"https://openalex.org/W2031817829","doi":"https://doi.org/10.1145/2487575.2487655","mag":"2031817829"},"language":"en","primary_location":{"id":"doi:10.1145/2487575.2487655","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2487575.2487655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM SIGKDD international 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/A5088316103","display_name":"Goce Ristanoski","orcid":"https://orcid.org/0000-0002-6097-411X"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Goce Ristanoski","raw_affiliation_strings":["The University of Melbourne, Melbourne, Australia","The University of Melbourne, Melbourne, Australia;"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]},{"raw_affiliation_string":"The University of Melbourne, Melbourne, Australia;","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062908102","display_name":"Wei Liu","orcid":"https://orcid.org/0000-0003-2968-2888"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["The University of Melbourne, Melbourne, Australia","The University of Melbourne, Melbourne, Australia;"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]},{"raw_affiliation_string":"The University of Melbourne, Melbourne, Australia;","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101609697","display_name":"James Bailey","orcid":"https://orcid.org/0000-0002-3769-3811"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"James Bailey","raw_affiliation_strings":["The University of Melbourne, Melbourne, Australia","The University of Melbourne, Melbourne, Australia;"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]},{"raw_affiliation_string":"The University of Melbourne, Melbourne, Australia;","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088316103"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":1.596,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.83378236,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"946","last_page":"954"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9983000159263611,"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":0.9983000159263611,"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/T10320","display_name":"Neural Networks and Applications","score":0.9901999831199646,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.989799976348877,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.867958664894104},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6910884976387024},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.689963698387146},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6727399826049805},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6684166789054871},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6000629663467407},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5699604153633118},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49021655321121216},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.473514586687088},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.47117167711257935},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4235178828239441},{"id":"https://openalex.org/keywords/online-machine-learning","display_name":"Online machine learning","score":0.4167414903640747},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3339582681655884},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21310406923294067},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.20315027236938477},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19847622513771057},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09657898545265198}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.867958664894104},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6910884976387024},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.689963698387146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6727399826049805},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6684166789054871},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6000629663467407},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5699604153633118},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49021655321121216},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.473514586687088},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.47117167711257935},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4235178828239441},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.4167414903640747},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3339582681655884},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21310406923294067},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.20315027236938477},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19847622513771057},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09657898545265198},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2487575.2487655","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2487575.2487655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM SIGKDD international 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":30,"referenced_works":["https://openalex.org/W93424348","https://openalex.org/W129077257","https://openalex.org/W1493047750","https://openalex.org/W1496558657","https://openalex.org/W1509580312","https://openalex.org/W1526027902","https://openalex.org/W1775626196","https://openalex.org/W1828498607","https://openalex.org/W1965974749","https://openalex.org/W1973222433","https://openalex.org/W1973579932","https://openalex.org/W1998792525","https://openalex.org/W2001603524","https://openalex.org/W2004787755","https://openalex.org/W2006664494","https://openalex.org/W2020944421","https://openalex.org/W2027182449","https://openalex.org/W2040883953","https://openalex.org/W2047298125","https://openalex.org/W2097227611","https://openalex.org/W2115364117","https://openalex.org/W2125793385","https://openalex.org/W2129330015","https://openalex.org/W2130487387","https://openalex.org/W2134769070","https://openalex.org/W2158236771","https://openalex.org/W2172073485","https://openalex.org/W2402068992","https://openalex.org/W2403962807","https://openalex.org/W2404205672"],"related_works":["https://openalex.org/W3194539120","https://openalex.org/W2937631562","https://openalex.org/W4361795583","https://openalex.org/W3195168932","https://openalex.org/W1996541855","https://openalex.org/W2979979539","https://openalex.org/W2101819884","https://openalex.org/W2803710604","https://openalex.org/W3127425528","https://openalex.org/W1929834138"],"abstract_inverted_index":{"Support":[0],"Vector":[1],"Machines":[2],"(SVMs)":[3],"are":[4],"a":[5,26,94,126],"leading":[6],"tool":[7],"in":[8,59,85,109],"machine":[9],"learning":[10,47,116],"and":[11],"have":[12],"been":[13],"used":[14],"with":[15,80],"considerable":[16],"success":[17],"for":[18,32],"the":[19,36,46,55,60,69,73,86,100,103,106,110,114,134,138],"task":[20],"of":[21,38,57,102,137],"time":[22,33,43,87],"series":[23,34,111],"forecasting.":[24],"However,":[25],"key":[27],"challenge":[28],"when":[29],"using":[30,125],"SVMs":[31],"is":[35,130],"question":[37],"how":[39],"to":[40,92],"deeply":[41],"integrate":[42],"elements":[44],"into":[45,113],"process.":[48,117],"To":[49],"address":[50],"this":[51],"challenge,":[52],"we":[53,67,76],"investigated":[54],"distribution":[56,81,107],"errors":[58],"forecasts":[61],"delivered":[62],"by":[63],"standard":[64],"SVMs.":[65],"Once":[66],"identified":[68],"samples":[70],"that":[71,83,124],"produced":[72],"largest":[74],"errors,":[75,139],"observed":[77],"their":[78],"correlation":[79],"shifts":[82,108],"occur":[84],"series.":[88],"This":[89],"motivated":[90],"us":[91],"propose":[93],"time-dependent":[95,127],"loss":[96,128],"function":[97,129],"which":[98,122],"allows":[99],"inclusion":[101],"information":[104],"about":[105],"directly":[112],"SVM":[115],"We":[118],"present":[119],"experimental":[120],"results":[121],"indicate":[123],"highly":[131],"promising,":[132],"reducing":[133],"overall":[135],"variance":[136],"as":[140,142],"well":[141],"delivering":[143],"more":[144],"accurate":[145],"predictions.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}