{"id":"https://openalex.org/W2550020085","doi":"https://doi.org/10.1109/ijcnn.2016.7727378","title":"Feature selection of autoregressive Neural Network inputs for trend Time Series Forecasting","display_name":"Feature selection of autoregressive Neural Network inputs for trend Time Series Forecasting","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2550020085","doi":"https://doi.org/10.1109/ijcnn.2016.7727378","mag":"2550020085"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5044304248","display_name":"Sven F. Crone","orcid":"https://orcid.org/0000-0003-4952-318X"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Sven F. Crone","raw_affiliation_strings":["Department of Management Science, Lancaster University Management School, Lancaster, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Department of Management Science, Lancaster University Management School, Lancaster, United Kingdom","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010677946","display_name":"Stephan Hager","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stephan Hager","raw_affiliation_strings":["IQAST Intelligent Forecasting Systems, RSG Software GmbH, Hamburg, Germany"],"affiliations":[{"raw_affiliation_string":"IQAST Intelligent Forecasting Systems, RSG Software GmbH, Hamburg, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5044304248"],"corresponding_institution_ids":["https://openalex.org/I67415387"],"apc_list":null,"apc_paid":null,"fwci":0.7455,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.79034183,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1515","last_page":"1522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.998199999332428,"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"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.998199999332428,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9969000220298767,"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":0.9799000024795532,"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/autoregressive-model","display_name":"Autoregressive model","score":0.8116270303726196},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6963448524475098},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6603783965110779},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6570546627044678},{"id":"https://openalex.org/keywords/lag","display_name":"Lag","score":0.6378220319747925},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6272678375244141},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.5590293407440186},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.48442724347114563},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4810545742511749},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4746188223361969},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.45813989639282227},{"id":"https://openalex.org/keywords/nonlinear-autoregressive-exogenous-model","display_name":"Nonlinear autoregressive exogenous model","score":0.438503623008728},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4194949269294739},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.4184838533401489},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4073110818862915},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3868100345134735},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12557318806648254}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.8116270303726196},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6963448524475098},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6603783965110779},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6570546627044678},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.6378220319747925},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6272678375244141},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.5590293407440186},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.48442724347114563},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4810545742511749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4746188223361969},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.45813989639282227},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.438503623008728},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4194949269294739},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.4184838533401489},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4073110818862915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3868100345134735},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12557318806648254},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.lancs.ac.uk:166199","is_oa":false,"landing_page_url":"https://eprints.lancs.ac.uk/id/eprint/166199/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Contribution in Book/Report/Proceedings"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.5,"display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1523944031","https://openalex.org/W1643663407","https://openalex.org/W1966351395","https://openalex.org/W1982348007","https://openalex.org/W1996605950","https://openalex.org/W2011227258","https://openalex.org/W2053865013","https://openalex.org/W2058117949","https://openalex.org/W2082332823","https://openalex.org/W2097180914","https://openalex.org/W2107071461","https://openalex.org/W2114840041","https://openalex.org/W2121324589","https://openalex.org/W2132782512","https://openalex.org/W2137643484","https://openalex.org/W2137983211","https://openalex.org/W2149905014","https://openalex.org/W2153787847","https://openalex.org/W2154326182","https://openalex.org/W3021982120","https://openalex.org/W4388063239","https://openalex.org/W6631434551","https://openalex.org/W6664658254"],"related_works":["https://openalex.org/W2606910468","https://openalex.org/W2734185189","https://openalex.org/W4382361623","https://openalex.org/W2981300935","https://openalex.org/W2799656149","https://openalex.org/W1545859685","https://openalex.org/W4213064089","https://openalex.org/W2589561664","https://openalex.org/W2990724441","https://openalex.org/W4206908315"],"abstract_inverted_index":{"The":[0],"capability":[1],"of":[2,16,30,47,62,80,98,116,136],"artificial":[3],"Neural":[4,43],"Networks":[5,44],"to":[6,130,147],"forecast":[7,150],"time":[8,85],"series":[9,86],"with":[10,70,87],"trends":[11,31,49],"has":[12,25],"been":[13],"a":[14,35,75,125,141],"topic":[15],"dispute.":[17],"While":[18],"selected":[19],"research":[20],"following":[21],"Zhang":[22],"and":[23,88,91,140],"Qi":[24],"indicated":[26],"that":[27,42,112,122],"prior":[28],"removal":[29],"is":[32,128],"required":[33,129],"for":[34,83],"Multilayer":[36],"Perceptron":[37],"(MLP),":[38],"others":[39],"provide":[40],"evidence":[41],"are":[45,114],"capable":[46,115],"forecasting":[48,117],"without":[50,89],"data":[51],"preprocessing,":[52],"either":[53],"by":[54,66],"choosing":[55],"input-nodes":[56],"employing":[57],"an":[58],"adequate":[59],"autoregressive":[60,81],"lag-structure":[61,127],"lagged":[63],"realisations":[64],"or":[65],"adding":[67],"explanatory":[68],"variables":[69],"trends.":[71],"This":[72],"paper":[73],"proposes":[74],"novel":[76],"variable":[77],"selection":[78,144],"methodology":[79],"lags":[82],"trended":[84],"seasonality,":[90],"assesses":[92],"its":[93],"efficacy":[94],"using":[95],"the":[96,99,134],"dataset":[97],"International":[100],"Time":[101],"Series":[102],"Forecasting":[103],"Competition":[104],"conducted":[105],"at":[106],"WCCI":[107],"2016.":[108],"Our":[109],"experiments":[110],"indicate":[111],"MLPs":[113],"different":[118],"trend":[119],"forms,":[120],"but":[121],"more":[123],"than":[124],"single":[126],"do":[131],"so,":[132],"making":[133],"use":[135],"multiple":[137],"input-lag":[138],"variants":[139],"robust":[142,149],"model":[143],"strategy":[145],"necessary":[146],"achieve":[148],"accuracy.":[151]},"counts_by_year":[{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
