{"id":"https://openalex.org/W3110472972","doi":"https://doi.org/10.1080/00401706.2022.2069158","title":"The Temporal Overfitting Problem with Applications in Wind Power Curve Modeling","display_name":"The Temporal Overfitting Problem with Applications in Wind Power Curve Modeling","publication_year":2022,"publication_date":"2022-04-22","ids":{"openalex":"https://openalex.org/W3110472972","doi":"https://doi.org/10.1080/00401706.2022.2069158","mag":"3110472972"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.2022.2069158","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2022.2069158","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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":"Technometrics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/journal_contribution/The_temporal_overfitting_problem_with_applications_in_wind_power_curve_modeling/19636775","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012427157","display_name":"Abhinav Prakash","orcid":"https://orcid.org/0000-0002-1792-8011"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav Prakash","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX"],"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052368885","display_name":"Rui Tuo","orcid":null},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Tuo","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX"],"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066523990","display_name":"Yu Ding","orcid":"https://orcid.org/0000-0001-6936-074X"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yu Ding","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX"],"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066523990"],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":null,"apc_paid":null,"fwci":2.3442,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.89715685,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"65","issue":"1","first_page":"70","last_page":"82"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9952999949455261,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9952999949455261,"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":0.9923999905586243,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9750000238418579,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/overfitting","display_name":"Overfitting","score":0.9581318497657776},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.6390544176101685},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6096364855766296},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.508120059967041},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44350674748420715},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.437109112739563},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4279903471469879},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4264291226863861},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.42049384117126465},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.41787058115005493},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37236636877059937},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2652352452278137},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2281927764415741},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.21308282017707825},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1780460774898529}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9581318497657776},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.6390544176101685},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6096364855766296},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.508120059967041},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44350674748420715},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.437109112739563},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4279903471469879},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4264291226863861},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.42049384117126465},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.41787058115005493},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37236636877059937},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2652352452278137},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2281927764415741},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.21308282017707825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1780460774898529},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/00401706.2022.2069158","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2022.2069158","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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":"Technometrics","raw_type":"journal-article"},{"id":"pmh:oai:figshare.com:article/19636775","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/The_temporal_overfitting_problem_with_applications_in_wind_power_curve_modeling/19636775","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"doi:10.6084/m9.figshare.19636775.v2","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.19636775.v2","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/19636775","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/The_temporal_overfitting_problem_with_applications_in_wind_power_curve_modeling/19636775","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7"}],"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":33,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1487825358","https://openalex.org/W1502922572","https://openalex.org/W1554944419","https://openalex.org/W1746819321","https://openalex.org/W1983472209","https://openalex.org/W1989681665","https://openalex.org/W2014828134","https://openalex.org/W2017108743","https://openalex.org/W2019678805","https://openalex.org/W2027973545","https://openalex.org/W2044118423","https://openalex.org/W2083536977","https://openalex.org/W2090373435","https://openalex.org/W2096850081","https://openalex.org/W2118091795","https://openalex.org/W2126292488","https://openalex.org/W2286642557","https://openalex.org/W2560136348","https://openalex.org/W2770863078","https://openalex.org/W2773188111","https://openalex.org/W2787894218","https://openalex.org/W2804076394","https://openalex.org/W2818855837","https://openalex.org/W2960466675","https://openalex.org/W2964037800","https://openalex.org/W2964104307","https://openalex.org/W3049522313","https://openalex.org/W3099006712","https://openalex.org/W3105261654","https://openalex.org/W4230674625","https://openalex.org/W4247545505","https://openalex.org/W4399650450"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W1982721348","https://openalex.org/W1493644805","https://openalex.org/W2371489490","https://openalex.org/W2130417215","https://openalex.org/W4225271882","https://openalex.org/W2003394143","https://openalex.org/W200497649"],"abstract_inverted_index":{"This":[0,48],"article":[1],"is":[2,50,89,103],"concerned":[3],"with":[4,141],"a":[5,65,76,97,106,114,168],"nonparametric":[6],"regression":[7],"problem":[8],"in":[9,19,40,43],"which":[10,56,102],"the":[11,15,24,70,83,110,132,173],"input":[12],"variables":[13],"and":[14,96,130,147],"errors":[16],"are":[17],"autocorrelated":[18],"time.":[20],"The":[21],"motivation":[22],"for":[23,64,150,167],"research":[25],"stems":[26],"from":[27,69,120,172],"modeling":[28],"wind":[29,156],"power":[30,144],"curves.":[31],"Using":[32],"existing":[33,143],"model":[34,41,88],"selection":[35],"methods,":[36],"like":[37],"cross-validation,":[38],"results":[39],"overfitting":[42,85,129,153],"presence":[44],"of":[45,59,101],"temporal":[46,54,84,128,152],"autocorrelation.":[47],"phenomenon":[49],"referred":[51],"to":[52,81,113,126],"as":[53],"overfitting,":[55],"causes":[57],"loss":[58],"performance":[60],"while":[61],"predicting":[62,165],"responses":[63],"time":[66,72,169,175],"domain":[67],"different":[68,171],"training":[71,174],"domain.":[73],"We":[74,108,135],"propose":[75],"Gaussian":[77],"process":[78],"(GP)-based":[79],"method":[80,112,140],"tackle":[82],"problem.":[86],"Our":[87,159],"partitioned":[90],"into":[91],"two":[92],"parts\u2014a":[93],"time-invariant":[94,133],"component":[95],"time-varying":[98],"component,":[99],"each":[100],"modeled":[104],"through":[105],"GP.":[107],"modify":[109],"inference":[111],"thinning-based":[115],"strategy,":[116],"an":[117],"idea":[118],"borrowed":[119],"Markov":[121],"chain":[122],"Monte":[123],"Carlo":[124],"sampling,":[125],"overcome":[127],"estimate":[131],"component.":[134],"extensively":[136],"compare":[137],"our":[138],"proposed":[139],"both":[142],"curve":[145],"models":[146],"available":[148],"ideas":[149],"handling":[151],"on":[154],"real":[155],"turbine":[157],"datasets.":[158],"approach":[160],"yields":[161],"significant":[162],"improvement":[163],"when":[164],"response":[166],"period":[170],"period.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}