{"id":"https://openalex.org/W2020701171","doi":"https://doi.org/10.1109/cdc.2012.6427042","title":"Learning from time series: Supervised Aggregative Feature Extraction","display_name":"Learning from time series: Supervised Aggregative Feature Extraction","publication_year":2012,"publication_date":"2012-12-01","ids":{"openalex":"https://openalex.org/W2020701171","doi":"https://doi.org/10.1109/cdc.2012.6427042","mag":"2020701171"},"language":"en","primary_location":{"id":"doi:10.1109/cdc.2012.6427042","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2012.6427042","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)","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/A5081650242","display_name":"Andrea Schirru","orcid":"https://orcid.org/0000-0002-9249-1227"},"institutions":[{"id":"https://openalex.org/I25217355","display_name":"University of Pavia","ror":"https://ror.org/00s6t1f81","country_code":"IT","type":"education","lineage":["https://openalex.org/I25217355"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Andrea Schirru","raw_affiliation_strings":["University of Pavia, Italy","\u2021University of Pavia, Italy,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pavia, Italy","institution_ids":["https://openalex.org/I25217355"]},{"raw_affiliation_string":"\u2021University of Pavia, Italy,","institution_ids":["https://openalex.org/I25217355"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026617079","display_name":"Gian Antonio Susto","orcid":"https://orcid.org/0000-0001-5739-9639"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Gian Antonio Susto","raw_affiliation_strings":["University of Padova, Italy","Univ. of Padova  (Italy)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Padova, Italy","institution_ids":["https://openalex.org/I138689650"]},{"raw_affiliation_string":"Univ. of Padova  (Italy)","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061018928","display_name":"Simone Pampuri","orcid":"https://orcid.org/0000-0002-0582-9892"},"institutions":[{"id":"https://openalex.org/I25217355","display_name":"University of Pavia","ror":"https://ror.org/00s6t1f81","country_code":"IT","type":"education","lineage":["https://openalex.org/I25217355"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Simone Pampuri","raw_affiliation_strings":["University of Pavia, Italy","\u2021University of Pavia, Italy,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pavia, Italy","institution_ids":["https://openalex.org/I25217355"]},{"raw_affiliation_string":"\u2021University of Pavia, Italy,","institution_ids":["https://openalex.org/I25217355"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005974306","display_name":"Se\u00e1n McLoone","orcid":"https://orcid.org/0000-0002-3016-6197"},"institutions":[{"id":"https://openalex.org/I157286207","display_name":"National University of Ireland, Maynooth","ror":"https://ror.org/048nfjm95","country_code":"IE","type":"education","lineage":["https://openalex.org/I157286207"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Sean McLoone","raw_affiliation_strings":["National University of Ireland Maynooth, Maynooth, Ireland","National University of Ireland Maynooth, Ireland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Ireland Maynooth, Maynooth, Ireland","institution_ids":["https://openalex.org/I157286207"]},{"raw_affiliation_string":"National University of Ireland Maynooth, Ireland","institution_ids":["https://openalex.org/I157286207"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9496,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.88825806,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5254","last_page":"5259"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9984999895095825,"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":0.9984999895095825,"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":0.9970999956130981,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9747999906539917,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8212243318557739},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6582317352294922},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5700443387031555},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5385864973068237},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.535362720489502},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5134414434432983},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4936555325984955},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4574020504951477},{"id":"https://openalex.org/keywords/reproducing-kernel-hilbert-space","display_name":"Reproducing kernel Hilbert space","score":0.4493570029735565},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4326103925704956},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42994001507759094},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4082590341567993},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3864879906177521},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3795335590839386},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.32410022616386414},{"id":"https://openalex.org/keywords/hilbert-space","display_name":"Hilbert space","score":0.27965307235717773},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2711848020553589}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8212243318557739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6582317352294922},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5700443387031555},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5385864973068237},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.535362720489502},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5134414434432983},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4936555325984955},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4574020504951477},{"id":"https://openalex.org/C80884492","wikidata":"https://www.wikidata.org/wiki/Q3345678","display_name":"Reproducing kernel Hilbert space","level":3,"score":0.4493570029735565},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4326103925704956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42994001507759094},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4082590341567993},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3864879906177521},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3795335590839386},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32410022616386414},{"id":"https://openalex.org/C62799726","wikidata":"https://www.wikidata.org/wiki/Q190056","display_name":"Hilbert space","level":2,"score":0.27965307235717773},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2711848020553589},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/cdc.2012.6427042","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2012.6427042","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.maynoothuniversity.ie:4228","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401432","display_name":"Maynooth University ePrints and eTheses Archive (Maynooth University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I157286207","host_organization_name":"National University of Ireland, Maynooth","host_organization_lineage":["https://openalex.org/I157286207"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Book Section"},{"id":"pmh:oai:pure.qub.ac.uk/portal:publications/9ad2d7eb-b42a-4136-b446-e7f31ddc8f79","is_oa":false,"landing_page_url":"https://pure.qub.ac.uk/en/publications/9ad2d7eb-b42a-4136-b446-e7f31ddc8f79","pdf_url":null,"source":{"id":"https://openalex.org/S4306402319","display_name":"Research Portal (Queen's University Belfast)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I126231945","host_organization_name":"Queen's University Belfast","host_organization_lineage":["https://openalex.org/I126231945"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Schirru , A , Susto , G A , Pampuri , S & McLoone , S 2012 , ' Learning from time series : Supervised Aggregative Feature Extraction ' , Proceedings of the IEEE Conference on Decision and Control , pp. 5254-5259 . https://doi.org/10.1109/CDC.2012.6427042","raw_type":"article"},{"id":"pmh:oai:www.research.unipd.it:11577/2714284","is_oa":false,"landing_page_url":"http://hdl.handle.net/11577/2714284","pdf_url":null,"source":{"id":"https://openalex.org/S4306402547","display_name":"Padua Research Archive (University of Padova)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1526146785","https://openalex.org/W1554944419","https://openalex.org/W1594563152","https://openalex.org/W1908291764","https://openalex.org/W1986280275","https://openalex.org/W2049351696","https://openalex.org/W2065995553","https://openalex.org/W2121802207","https://openalex.org/W2159544029","https://openalex.org/W2171033594","https://openalex.org/W2346456480","https://openalex.org/W2971788173","https://openalex.org/W4246024215","https://openalex.org/W6631556622","https://openalex.org/W6704782557","https://openalex.org/W6767293667"],"related_works":["https://openalex.org/W2146739527","https://openalex.org/W2065805792","https://openalex.org/W4285290579","https://openalex.org/W149320920","https://openalex.org/W3125885229","https://openalex.org/W1527525543","https://openalex.org/W2370512383","https://openalex.org/W1835315728","https://openalex.org/W2962818398","https://openalex.org/W2111121652"],"abstract_inverted_index":{"Many":[0],"modeling":[1],"problems":[2,16],"require":[3],"to":[4,43,72,77,93,107,161,168,177],"estimate":[5],"a":[6,22,36,74,85,123],"scalar":[7],"output":[8],"from":[9,27],"one":[10],"or":[11],"more":[12],"time":[13,29,65,113],"series.":[14],"Such":[15],"are":[17,55,175],"usually":[18,51],"tackled":[19],"by":[20,58],"extracting":[21],"fixed":[23],"number":[24],"of":[25,67,112,141,153,181],"features":[26],"the":[28,95,98,149,154,159,179,182],"series":[30,66,114],"(like":[31],"their":[32],"statistical":[33],"moments),":[34],"with":[35,79],"consequent":[37],"loss":[38],"in":[39],"information":[40,147],"that":[41,54],"leads":[42],"suboptimal":[44],"predictive":[45,171],"models.":[46,172],"Moreover,":[47],"feature":[48,188],"extraction":[49],"techniques":[50],"make":[52],"assumptions":[53],"not":[56],"met":[57],"real":[59],"world":[60],"settings":[61],"(e.g.":[62],"uniformly":[63],"sampled":[64],"constant":[68],"length),":[69],"and":[70,143,158],"fail":[71],"deliver":[73],"thorough":[75],"methodology":[76,86,184],"deal":[78],"noisy":[80],"data.":[81],"In":[82],"this":[83],"paper":[84],"based":[87],"on":[88],"functional":[89,156],"learning":[90],"is":[91],"proposed":[92,99],"overcome":[94],"aforementioned":[96],"problems;":[97],"Supervised":[100],"Aggregative":[101],"Feature":[102],"Extraction":[103],"(SAFE)":[104],"approach":[105],"allows":[106],"derive":[108],"continuous,":[109],"smooth":[110],"estimates":[111],"data":[115],"(yielding":[116],"aggregate":[117],"local":[118],"information),":[119],"while":[120],"simultaneously":[121],"estimating":[122],"continuous":[124],"shape":[125],"function":[126],"yielding":[127],"optimal":[128],"predictions.":[129],"The":[130],"SAFE":[131],"paradigm":[132],"enjoys":[133],"several":[134],"properties":[135],"like":[136],"closed":[137],"form":[138],"solution,":[139],"incorporation":[140],"first":[142],"second":[144],"order":[145],"derivative":[146],"into":[148],"regressor":[150],"matrix,":[151],"interpretability":[152],"generated":[155],"predictor":[157],"possibility":[160],"exploit":[162],"Reproducing":[163],"Kernel":[164],"Hilbert":[165],"Spaces":[166],"setting":[167],"yield":[169],"nonlinear":[170],"Simulation":[173],"studies":[174],"provided":[176],"highlight":[178],"strengths":[180],"new":[183],"w.r.t.":[185],"standard":[186],"unsupervised":[187],"selection":[189],"approaches.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}