{"id":"https://openalex.org/W2778633435","doi":"https://doi.org/10.1186/s13634-017-0518-4","title":"Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time","display_name":"Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2778633435","doi":"https://doi.org/10.1186/s13634-017-0518-4","mag":"2778633435"},"language":"en","primary_location":{"id":"doi:10.1186/s13634-017-0518-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-017-0518-4","pdf_url":"https://asp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13634-017-0518-4","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13634-017-0518-4","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040717332","display_name":"I\u00f1igo Urteaga","orcid":"https://orcid.org/0000-0003-3656-0037"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"I\u00f1igo Urteaga","raw_affiliation_strings":["Department of Applied Physics and Applied Mathematics, Columbia University, New York, 10027, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Applied Physics and Applied Mathematics, Columbia University, New York, 10027, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058778434","display_name":"M\u00f3nica F. Bugallo","orcid":"https://orcid.org/0000-0003-2963-1474"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M\u00f3nica F. Bugallo","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, 11794, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, 11794, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006962534","display_name":"Petar M. Djuri\u0107","orcid":"https://orcid.org/0000-0001-7791-3199"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Petar M. Djuri\u0107","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, 11794, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, 11794, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5040717332"],"corresponding_institution_ids":["https://openalex.org/I78577930"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":0.2079,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66654028,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"2017","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9941999912261963,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9941999912261963,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9901999831199646,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9868000149726868,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6841098666191101},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6366920471191406},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6068320274353027},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5817127227783203},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5599043965339661},{"id":"https://openalex.org/keywords/autoregressive\u2013moving-average-model","display_name":"Autoregressive\u2013moving-average model","score":0.4829920530319214},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45783519744873047},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.452747642993927},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3928685486316681},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.38614070415496826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3626491129398346},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3506074547767639},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3138178586959839},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.26722753047943115},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2607371211051941},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.1966780126094818},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08417892456054688}],"concepts":[{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6841098666191101},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6366920471191406},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6068320274353027},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5817127227783203},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5599043965339661},{"id":"https://openalex.org/C74883015","wikidata":"https://www.wikidata.org/wiki/Q290467","display_name":"Autoregressive\u2013moving-average model","level":3,"score":0.4829920530319214},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45783519744873047},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.452747642993927},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3928685486316681},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.38614070415496826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3626491129398346},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3506074547767639},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3138178586959839},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26722753047943115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2607371211051941},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.1966780126094818},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08417892456054688},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s13634-017-0518-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-017-0518-4","pdf_url":"https://asp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13634-017-0518-4","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:0bac814102c24be5886c97a7c1bba59f","is_oa":true,"landing_page_url":"https://doaj.org/article/0bac814102c24be5886c97a7c1bba59f","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2017, Iss 1, Pp 1-15 (2017)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13634-017-0518-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-017-0518-4","pdf_url":"https://asp-eurasipjournals.springeropen.com/track/pdf/10.1186/s13634-017-0518-4","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5099999904632568,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G2194954074","display_name":null,"funder_award_id":"CCF-1618999","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3600570191","display_name":"CIF: Small: Dynamic Networks: Learning, Inference, and Prediction with Nonparametric Bayesian Methods","funder_award_id":"1618999","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3853206302","display_name":"CIF: Small: Advancing Adaptive Importance Sampling for Signal Processing","funder_award_id":"1617986","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4812324844","display_name":null,"funder_award_id":"CCF-1617986","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2778633435.pdf","grobid_xml":"https://content.openalex.org/works/W2778633435.grobid-xml"},"referenced_works_count":61,"referenced_works":["https://openalex.org/W357154381","https://openalex.org/W1483307070","https://openalex.org/W1486034269","https://openalex.org/W1512184319","https://openalex.org/W1513008779","https://openalex.org/W1520710862","https://openalex.org/W1566395535","https://openalex.org/W1606059061","https://openalex.org/W1762449885","https://openalex.org/W1788219820","https://openalex.org/W1979331645","https://openalex.org/W1999674105","https://openalex.org/W2001250891","https://openalex.org/W2029027380","https://openalex.org/W2032886429","https://openalex.org/W2040196349","https://openalex.org/W2046357557","https://openalex.org/W2064517238","https://openalex.org/W2065266611","https://openalex.org/W2066041990","https://openalex.org/W2073139692","https://openalex.org/W2085157190","https://openalex.org/W2092559013","https://openalex.org/W2098613108","https://openalex.org/W2105934661","https://openalex.org/W2116807840","https://openalex.org/W2123487311","https://openalex.org/W2127406017","https://openalex.org/W2140745919","https://openalex.org/W2153155589","https://openalex.org/W2159605934","https://openalex.org/W2160337655","https://openalex.org/W2165609874","https://openalex.org/W2166129575","https://openalex.org/W2167442255","https://openalex.org/W2204230881","https://openalex.org/W2241325635","https://openalex.org/W2271361270","https://openalex.org/W2294875987","https://openalex.org/W2319085245","https://openalex.org/W2341760625","https://openalex.org/W2480874920","https://openalex.org/W2488418300","https://openalex.org/W2497534953","https://openalex.org/W2515453889","https://openalex.org/W2537259742","https://openalex.org/W2799461614","https://openalex.org/W2952937083","https://openalex.org/W2952962174","https://openalex.org/W3104382201","https://openalex.org/W3107394559","https://openalex.org/W3121154744","https://openalex.org/W3121895803","https://openalex.org/W3125096521","https://openalex.org/W4231057675","https://openalex.org/W4232653939","https://openalex.org/W4251281949","https://openalex.org/W4255133955","https://openalex.org/W4292963524","https://openalex.org/W4388063301","https://openalex.org/W6789457870"],"related_works":["https://openalex.org/W2151689585","https://openalex.org/W2380816257","https://openalex.org/W3087071515","https://openalex.org/W4283726152","https://openalex.org/W2185837235","https://openalex.org/W3097417252","https://openalex.org/W2021955905","https://openalex.org/W3115491726","https://openalex.org/W2380210200","https://openalex.org/W2105668867"],"abstract_inverted_index":{"We":[0,20,37,78,118],"consider":[1],"the":[2,35,53,56,62,120,123,130],"problem":[3],"of":[4,7,29,34,46,55,71,82,87,101,122,129],"sequential":[5,73],"inference":[6,86],"latent":[8,88],"time-series":[9,41,90],"with":[10,24,91,114],"innovations":[11,92],"correlated":[12,93],"in":[13,94,99,106],"time":[14,59,64,95],"and":[15,109],"observed":[16],"via":[17],"nonlinear":[18],"functions.":[19],"accommodate":[21],"time-varying":[22],"phenomena":[23],"diverse":[25,115],"properties":[26],"by":[27,42,126],"means":[28],"a":[30,43,80,107],"flexible":[31],"mathematical":[32],"representation":[33],"data.":[36],"characterize":[38],"statistically":[39],"such":[40],"Bayesian":[44],"analysis":[45],"their":[47],"densities.":[48],"The":[49,103],"density":[50],"that":[51],"describes":[52],"transition":[54],"state":[57],"from":[58],"t":[60],"to":[61],"next":[63],"instant":[65],"t+1":[66],"is":[67],"used":[68],"for":[69,85,96,112],"implementation":[70],"novel":[72],"Monte":[74],"Carlo":[75],"(SMC)":[76],"methods.":[77],"present":[79],"set":[81],"SMC":[83],"methods":[84,104],"ARMA":[89],"different":[97],"assumptions":[98],"knowledge":[100],"parameters.":[102],"operate":[105],"unified":[108],"consistent":[110],"manner":[111],"data":[113],"memory":[116],"properties.":[117],"show":[119],"validity":[121],"proposed":[124],"approach":[125],"comprehensive":[127],"simulations":[128],"challenging":[131],"stochastic":[132],"volatility":[133],"model.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}