{"id":"https://openalex.org/W2157398049","doi":"https://doi.org/10.1145/1401890.1401911","title":"Semi-supervised learning with data calibration for long-term time series forecasting","display_name":"Semi-supervised learning with data calibration for long-term time series forecasting","publication_year":2008,"publication_date":"2008-08-24","ids":{"openalex":"https://openalex.org/W2157398049","doi":"https://doi.org/10.1145/1401890.1401911","mag":"2157398049"},"language":"en","primary_location":{"id":"doi:10.1145/1401890.1401911","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401911","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th 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/A5111603892","display_name":"Haibin Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Haibin Cheng","raw_affiliation_strings":["Michigan State University, East Lansing, MI, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071546444","display_name":"Pang\u2010Ning Tan","orcid":"https://orcid.org/0000-0003-3205-0339"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pang-Ning Tan","raw_affiliation_strings":["Michigan State University, East Lansing, MI, USA"],"affiliations":[{"raw_affiliation_string":"Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111603892"],"corresponding_institution_ids":["https://openalex.org/I87216513"],"apc_list":null,"apc_paid":null,"fwci":4.1782,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.94521173,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"133","last_page":"141"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9969000220298767,"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":0.9969000220298767,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9926999807357788,"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":0.7480124831199646},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6365746259689331},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6210492849349976},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6101354360580444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5248311161994934},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.47611886262893677},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4427741467952728},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4220079183578491},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.41106492280960083},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3852353096008301},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20141717791557312},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0884377658367157}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7480124831199646},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6365746259689331},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6210492849349976},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6101354360580444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5248311161994934},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.47611886262893677},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4427741467952728},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4220079183578491},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.41106492280960083},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3852353096008301},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20141717791557312},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0884377658367157},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1401890.1401911","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401911","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.151.2916","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.2916","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cse.msu.edu/~ptan/papers/KDD08.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.800000011920929,"display_name":"Climate action"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W57680428","https://openalex.org/W189274229","https://openalex.org/W605575788","https://openalex.org/W1493898446","https://openalex.org/W1517272581","https://openalex.org/W1527119328","https://openalex.org/W1554457642","https://openalex.org/W1585385982","https://openalex.org/W1599300385","https://openalex.org/W1939419584","https://openalex.org/W1964357740","https://openalex.org/W1997255626","https://openalex.org/W2013092224","https://openalex.org/W2048679005","https://openalex.org/W2058177657","https://openalex.org/W2070762331","https://openalex.org/W2071601478","https://openalex.org/W2107008379","https://openalex.org/W2114027834","https://openalex.org/W2134197702","https://openalex.org/W2136504847","https://openalex.org/W2136540032","https://openalex.org/W2139823104","https://openalex.org/W2139960518","https://openalex.org/W2140676093","https://openalex.org/W2141703670","https://openalex.org/W2143349618","https://openalex.org/W2146871184","https://openalex.org/W2153149612","https://openalex.org/W2154455818","https://openalex.org/W2161180943","https://openalex.org/W2170569305","https://openalex.org/W2170938446","https://openalex.org/W2990138404","https://openalex.org/W4285719527","https://openalex.org/W6631540155","https://openalex.org/W6682494755"],"related_works":["https://openalex.org/W2364370872","https://openalex.org/W2053269318","https://openalex.org/W2025614924","https://openalex.org/W2294335174","https://openalex.org/W2097963413","https://openalex.org/W3145575561","https://openalex.org/W2001275470","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Many":[0],"time":[1,156,217],"series":[2,157,218],"prediction":[3,13,28],"methods":[4,214],"have":[5],"focused":[6],"on":[7,135,160,189],"single":[8],"step":[9,29],"or":[10,83],"short":[11],"term":[12,54,69],"problems":[14],"due":[15],"to":[16,30,85,96,140,172,210],"the":[17,22,31,101,116,142,175,204,207],"inherent":[18],"difficulty":[19],"in":[20,125],"controlling":[21],"propagation":[23],"of":[24,40,76,89,177,195,206],"errors":[25],"from":[26,192],"one":[27],"next":[32],"step.":[33],"Yet,":[34],"there":[35,92],"is":[36,138,169],"a":[37,126,131,150,193],"broad":[38],"range":[39],"applications":[41],"such":[42,107],"as":[43,108],"climate":[44,110,198],"impact":[45],"assessments":[46],"and":[47,111,181],"urban":[48],"growth":[49],"planning":[50],"that":[51,65],"require":[52,72],"long":[53,68],"forecasting":[55,158],"capabilities":[56],"for":[57,100,154,215],"strategic":[58],"decision":[59],"making.":[60],"Training":[61],"an":[62,73],"accurate":[63],"model":[64,133,182],"produces":[66],"reliable":[67],"predictions":[70],"would":[71],"extensive":[74],"amount":[75],"historical":[77,180],"data,":[78],"which":[79],"are":[80,93,122],"either":[81],"unavailable":[82],"expensive":[84],"acquire.":[86],"For":[87],"some":[88],"these":[90,120],"domains,":[91,196],"alternative":[94],"ways":[95],"generate":[97],"potential":[98],"scenarios":[99],"future":[102,143],"using":[103],"computer-driven":[104],"simulation":[105,183],"models,":[106],"global":[109],"traffic":[112],"demand":[113],"models.":[114],"However,":[115],"data":[117,190],"generated":[118],"by":[119],"models":[121],"currently":[123],"utilized":[124],"supervised":[127,212],"learning":[128,152,213],"setting,":[129],"where":[130],"predictive":[132],"trained":[134],"past":[136],"observations":[137],"used":[139],"estimate":[141],"values.":[144],"In":[145],"this":[146],"paper,":[147],"we":[148],"present":[149],"semi-supervised":[151],"framework":[153],"long-term":[155,216],"based":[159],"Hidden":[161],"Markov":[162],"Model":[163],"Regression.":[164],"A":[165],"covariance":[166],"alignment":[167],"method":[168],"also":[170],"developed":[171],"deal":[173],"with":[174],"issue":[176],"inconsistencies":[178],"between":[179],"data.":[184],"We":[185],"evaluated":[186],"our":[187],"approach":[188,208],"sets":[191],"variety":[194],"including":[197],"modeling.":[199],"Our":[200],"experimental":[201],"results":[202],"demonstrate":[203],"efficacy":[205],"compared":[209],"other":[211],"forecasting.":[219]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}