{"id":"https://openalex.org/W3217031555","doi":"https://doi.org/10.1145/3480971","title":"KOMPOS: Connecting Causal Knots in Large Nonlinear Time Series with Non-Parametric Regression Splines","display_name":"KOMPOS: Connecting Causal Knots in Large Nonlinear Time Series with Non-Parametric Regression Splines","publication_year":2021,"publication_date":"2021-10-31","ids":{"openalex":"https://openalex.org/W3217031555","doi":"https://doi.org/10.1145/3480971","mag":"3217031555"},"language":"en","primary_location":{"id":"doi:10.1145/3480971","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3480971","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","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/A5028804372","display_name":"Georgios Koutroulis","orcid":null},"institutions":[{"id":"https://openalex.org/I4210127322","display_name":"Procomcure Biotech (Austria)","ror":"https://ror.org/035g5pd63","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210127322"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Georgios Koutroulis","raw_affiliation_strings":["Pro2Future GmbH, Inffeldgasse, Graz, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pro2Future GmbH, Inffeldgasse, Graz, Austria","institution_ids":["https://openalex.org/I4210127322"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008239279","display_name":"Leo Botler","orcid":"https://orcid.org/0000-0002-4683-2422"},"institutions":[{"id":"https://openalex.org/I4092182","display_name":"Graz University of Technology","ror":"https://ror.org/00d7xrm67","country_code":"AT","type":"education","lineage":["https://openalex.org/I4092182"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Leo Botler","raw_affiliation_strings":["Graz University of Technology, Graz, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graz University of Technology, Graz, Austria","institution_ids":["https://openalex.org/I4092182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062199290","display_name":"Belgin Mutlu","orcid":"https://orcid.org/0000-0002-6910-2780"},"institutions":[{"id":"https://openalex.org/I4210127322","display_name":"Procomcure Biotech (Austria)","ror":"https://ror.org/035g5pd63","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210127322"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Belgin Mutlu","raw_affiliation_strings":["Pro2Future GmbH, Inffeldgasse, Graz, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pro2Future GmbH, Inffeldgasse, Graz, Austria","institution_ids":["https://openalex.org/I4210127322"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076171782","display_name":"Konrad Diwold","orcid":"https://orcid.org/0000-0002-6265-4064"},"institutions":[{"id":"https://openalex.org/I4092182","display_name":"Graz University of Technology","ror":"https://ror.org/00d7xrm67","country_code":"AT","type":"education","lineage":["https://openalex.org/I4092182"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Konrad Diwold","raw_affiliation_strings":["Pro2Future GmbH and Graz University of Technology, Graz, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pro2Future GmbH and Graz University of Technology, Graz, Austria","institution_ids":["https://openalex.org/I4092182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038885496","display_name":"Kay R\u00f6mer","orcid":"https://orcid.org/0000-0002-4248-4424"},"institutions":[{"id":"https://openalex.org/I4092182","display_name":"Graz University of Technology","ror":"https://ror.org/00d7xrm67","country_code":"AT","type":"education","lineage":["https://openalex.org/I4092182"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Kay R\u00f6mer","raw_affiliation_strings":["Graz University of Technology, Graz, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graz University of Technology, Graz, Austria","institution_ids":["https://openalex.org/I4092182"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014398832","display_name":"Roman Kern","orcid":"https://orcid.org/0000-0003-0202-6100"},"institutions":[{"id":"https://openalex.org/I4092182","display_name":"Graz University of Technology","ror":"https://ror.org/00d7xrm67","country_code":"AT","type":"education","lineage":["https://openalex.org/I4092182"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Roman Kern","raw_affiliation_strings":["Graz University of Technology, Graz, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graz University of Technology, Graz, Austria","institution_ids":["https://openalex.org/I4092182"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5028804372"],"corresponding_institution_ids":["https://openalex.org/I4210127322"],"apc_list":null,"apc_paid":null,"fwci":0.935,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.78647996,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"12","issue":"5","first_page":"1","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9836000204086304,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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/T10136","display_name":"Statistical Methods and Inference","score":0.9836000204086304,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.975600004196167,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.970300018787384,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7498827576637268},{"id":"https://openalex.org/keywords/causal-structure","display_name":"Causal structure","score":0.6521626710891724},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5326921939849854},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4966490864753723},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.48746657371520996},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.48114216327667236},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4584280252456665},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44947925209999084},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.422184020280838},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4173582196235657},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38620877265930176},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3561430871486664},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19023564457893372},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14192384481430054}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7498827576637268},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.6521626710891724},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5326921939849854},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4966490864753723},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.48746657371520996},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.48114216327667236},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4584280252456665},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44947925209999084},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.422184020280838},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4173582196235657},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38620877265930176},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3561430871486664},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19023564457893372},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14192384481430054},{"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/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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3480971","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3480971","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1642849371","https://openalex.org/W1933386641","https://openalex.org/W1972393611","https://openalex.org/W1995691260","https://openalex.org/W2056944867","https://openalex.org/W2063978378","https://openalex.org/W2099610690","https://openalex.org/W2102201073","https://openalex.org/W2110065044","https://openalex.org/W2118418963","https://openalex.org/W2127635108","https://openalex.org/W2132547334","https://openalex.org/W2132555912","https://openalex.org/W2135046866","https://openalex.org/W2135631431","https://openalex.org/W2143891888","https://openalex.org/W2151226328","https://openalex.org/W2154195147","https://openalex.org/W2165582599","https://openalex.org/W2171408260","https://openalex.org/W2178225550","https://openalex.org/W2182886880","https://openalex.org/W2562162676","https://openalex.org/W2621189008","https://openalex.org/W2789810996","https://openalex.org/W2805346424","https://openalex.org/W2807340117","https://openalex.org/W2908623803","https://openalex.org/W2921112192","https://openalex.org/W2948579453","https://openalex.org/W2949505632","https://openalex.org/W2963371984","https://openalex.org/W2963602075","https://openalex.org/W2964132737","https://openalex.org/W2966035132","https://openalex.org/W2980286315","https://openalex.org/W2998265369","https://openalex.org/W3026030561","https://openalex.org/W3085120741","https://openalex.org/W3104466517","https://openalex.org/W3121988858","https://openalex.org/W3129194741","https://openalex.org/W3130876899","https://openalex.org/W3143835353"],"related_works":["https://openalex.org/W2950035905","https://openalex.org/W2072483141","https://openalex.org/W3098259173","https://openalex.org/W2753729722","https://openalex.org/W3123305972","https://openalex.org/W4249439171","https://openalex.org/W2964187211","https://openalex.org/W3098638521","https://openalex.org/W2218619375","https://openalex.org/W2724547889"],"abstract_inverted_index":{"Recovering":[0],"causality":[1],"from":[2,83,100],"copious":[3],"time":[4,51,174],"series":[5],"data":[6,27],"beyond":[7],"mere":[8],"correlations":[9],"has":[10],"been":[11],"an":[12],"important":[13],"contributing":[14],"factor":[15],"in":[16,25,64],"numerous":[17],"scientific":[18],"fields.":[19],"Most":[20],"existing":[21,161],"works":[22],"assume":[23],"linearity":[24],"the":[26,45,49,112,136,140,154,157,167],"that":[28,78],"may":[29],"not":[30,40,127],"comply":[31],"with":[32,90,105],"many":[33],"real-world":[34,151],"scenarios.":[35],"Moreover,":[36],"it":[37],"is":[38,55,126],"usually":[39],"sufficient":[41],"to":[42,121,129,135],"solely":[43],"infer":[44],"causal":[46,84,98,114,162],"relationships.":[47],"Identifying":[48],"correct":[50],"delay":[52],"of":[53,86,139,156,170],"cause-effect":[54],"extremely":[56],"vital":[57],"for":[58,166],"further":[59],"insight":[60],"and":[61,150,172],"effective":[62],"policies":[63],"inter-disciplinary":[65],"domains.":[66],"To":[67],"bridge":[68],"this":[69],"gap,":[70],"we":[71],"propose":[72],"KOMPOS,":[73],"a":[74,80],"novel":[75],"algorithmic":[76],"framework":[77],"combines":[79],"powerful":[81],"concept":[82],"discovery":[85,163],"additive":[87,107],"noise":[88,133],"models":[89],"graphical":[91],"ones.":[92],"We":[93,143],"primarily":[94],"build":[95],"our":[96,124],"structural":[97],"model":[99],"multivariate":[101],"adaptive":[102],"regression":[103,141],"splines":[104],"inherent":[106],"local":[108],"nonlinearities,":[109],"which":[110],"render":[111],"underlying":[113],"structure":[115],"more":[116],"easily":[117],"identifiable.":[118],"In":[119],"contrast":[120],"other":[122],"methods,":[123,164],"approach":[125],"restricted":[128],"Gaussian":[130],"or":[131],"non-Gaussian":[132],"due":[134],"non-parametric":[137],"attribute":[138],"method.":[142],"conduct":[144],"extensive":[145],"experiments":[146],"on":[147],"both":[148],"synthetic":[149],"datasets,":[152],"demonstrating":[153],"superiority":[155],"proposed":[158],"algorithm":[159],"over":[160],"especially":[165],"challenging":[168],"cases":[169],"autocorrelated":[171],"non-stationary":[173],"series.":[175]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}