{"id":"https://openalex.org/W2974237113","doi":"https://doi.org/10.1080/03610918.2019.1664578","title":"A new approach for the vector forecast algorithm in singular spectrum analysis","display_name":"A new approach for the vector forecast algorithm in singular spectrum analysis","publication_year":2019,"publication_date":"2019-09-16","ids":{"openalex":"https://openalex.org/W2974237113","doi":"https://doi.org/10.1080/03610918.2019.1664578","mag":"2974237113"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2019.1664578","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1664578","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"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":"Communications in Statistics - Simulation and Computation","raw_type":"journal-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/A5024974930","display_name":"Paulo Canas Rodrigues","orcid":"https://orcid.org/0000-0002-1248-9910"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]},{"id":"https://openalex.org/I126158947","display_name":"Universidade Federal da Bahia","ror":"https://ror.org/03k3p7647","country_code":"BR","type":"education","lineage":["https://openalex.org/I126158947"]}],"countries":["BR","FI"],"is_corresponding":true,"raw_author_name":"Paulo Canas Rodrigues","raw_affiliation_strings":["CAST - Center for Applied Statistics and Data Analytics, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland;","Department of Statistics, Federal University of Bahia, Salvador, BA, Brazil;"],"affiliations":[{"raw_affiliation_string":"CAST - Center for Applied Statistics and Data Analytics, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland;","institution_ids":["https://openalex.org/I166825849"]},{"raw_affiliation_string":"Department of Statistics, Federal University of Bahia, Salvador, BA, Brazil;","institution_ids":["https://openalex.org/I126158947"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014899136","display_name":"Rahim Mahmoudvand","orcid":"https://orcid.org/0000-0003-2157-0582"},"institutions":[{"id":"https://openalex.org/I167694869","display_name":"Bu-Ali Sina University","ror":"https://ror.org/04ka8rx28","country_code":"IR","type":"education","lineage":["https://openalex.org/I167694869"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Rahim Mahmoudvand","raw_affiliation_strings":["Department of Statistics, Bu-Ali Sina University, Hamedan, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Bu-Ali Sina University, Hamedan, Iran","institution_ids":["https://openalex.org/I167694869"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5024974930"],"corresponding_institution_ids":["https://openalex.org/I126158947","https://openalex.org/I166825849"],"apc_list":null,"apc_paid":null,"fwci":1.9882,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.85884903,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"49","issue":"3","first_page":"591","last_page":"605"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13487","display_name":"Statistical and numerical algorithms","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13487","display_name":"Statistical and numerical algorithms","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10924","display_name":"Cardiovascular Health and Disease Prevention","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T14369","display_name":"Diverse Interdisciplinary Research Innovations","score":0.9003000259399414,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/singular-spectrum-analysis","display_name":"Singular spectrum analysis","score":0.8856741189956665},{"id":"https://openalex.org/keywords/ibm","display_name":"IBM","score":0.6405915021896362},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.633771538734436},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5980584621429443},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.5771346688270569},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.560152530670166},{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.4185112416744232},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33265796303749084},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.12307441234588623}],"concepts":[{"id":"https://openalex.org/C136272165","wikidata":"https://www.wikidata.org/wiki/Q4048889","display_name":"Singular spectrum analysis","level":3,"score":0.8856741189956665},{"id":"https://openalex.org/C70388272","wikidata":"https://www.wikidata.org/wiki/Q5968558","display_name":"IBM","level":2,"score":0.6405915021896362},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.633771538734436},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5980584621429443},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.5771346688270569},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.560152530670166},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.4185112416744232},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33265796303749084},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.12307441234588623},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","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/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","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":1,"locations":[{"id":"doi:10.1080/03610918.2019.1664578","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1664578","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"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":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1563281071","https://openalex.org/W1926647722","https://openalex.org/W1981693973","https://openalex.org/W2044421505","https://openalex.org/W2107595860","https://openalex.org/W2341049598","https://openalex.org/W2738796499","https://openalex.org/W2748099595","https://openalex.org/W2761731804","https://openalex.org/W2962891490","https://openalex.org/W4232129301","https://openalex.org/W4297709905"],"related_works":["https://openalex.org/W3126131865","https://openalex.org/W4253186488","https://openalex.org/W2044344400","https://openalex.org/W1996938127","https://openalex.org/W2083611981","https://openalex.org/W2072507639","https://openalex.org/W2099549249","https://openalex.org/W2362717763","https://openalex.org/W4241139206","https://openalex.org/W2501514717"],"abstract_inverted_index":{"The":[0,99],"window":[1,23,90],"length,":[2],"L,":[3],"is":[4,36],"the":[5,66,73,89,140],"first":[6],"parameter":[7],"that":[8,139],"must":[9],"be":[10],"specified":[11],"in":[12,72,143],"Singular":[13],"Spectrum":[14],"Analysis":[15],"(SSA)":[16],"for":[17,62,88,93,97],"time":[18],"series":[19],"analysis.":[20],"A":[21],"large":[22],"length":[24],"has":[25],"a":[26,30,40,49],"potential":[27],"to":[28,38,83],"produce":[29,39],"good":[31],"model":[32,54,79],"fit,":[33],"but":[34],"it":[35],"unlikely":[37],"parsimonious":[41,51],"forecasting":[42,53,76],"model.":[43],"In":[44],"this":[45,144],"paper,":[46],"we":[47],"propose":[48],"new":[50],"vector":[52,75],"which":[55],"uses":[56],"an":[57],"optimal":[58],"m":[59],"(<L\u22121)":[60],"coefficients":[61,70],"forecasting,":[63],"instead":[64],"of":[65,115],"L":[67],"\u2013":[68],"1":[69,126],"used":[71],"standard":[74,102],"method.":[77],"This":[78],"enables":[80],"SSA":[81],"users":[82],"consider":[84],"two":[85],"different":[86],"values":[87],"length:":[91],"one":[92],"reconstruction":[94],"and":[95,101,107,110,128],"another":[96],"forecasting.":[98],"proposed":[100,142],"methods":[103],"are":[104],"compared":[105],"methodologically":[106],"also":[108],"implemented":[109],"tested":[111],"on":[112],"daily":[113],"observations":[114],"six":[116],"stocks:":[117],"AAPL,":[118],"AMZN,":[119],"EBAY,":[120],"IBM,":[121],"INTC,":[122],"MSFT,":[123],"between":[124],"Jan":[125],"2000":[127],"Dec":[129],"31":[130],"2015,":[131],"each":[132],"including":[133],"4025":[134],"observations.":[135],"It":[136],"was":[137],"found":[138],"method":[141],"paper":[145],"provides":[146],"major":[147],"improvements":[148],"regarding":[149],"forecast":[150],"accuracy.":[151]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
