{"id":"https://openalex.org/W4403331710","doi":"https://doi.org/10.23919/fusion59988.2024.10706404","title":"A Gaussian Process-based Streaming Algorithm for Prediction of Time Series With Regimes and Outliers","display_name":"A Gaussian Process-based Streaming Algorithm for Prediction of Time Series With Regimes and Outliers","publication_year":2024,"publication_date":"2024-07-08","ids":{"openalex":"https://openalex.org/W4403331710","doi":"https://doi.org/10.23919/fusion59988.2024.10706404"},"language":"en","primary_location":{"id":"doi:10.23919/fusion59988.2024.10706404","is_oa":false,"landing_page_url":"https://doi.org/10.23919/fusion59988.2024.10706404","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 27th International Conference on Information Fusion (FUSION)","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/A5037977097","display_name":"Daniel Waxman","orcid":"https://orcid.org/0009-0004-0168-5547"},"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":true,"raw_author_name":"Daniel Waxman","raw_affiliation_strings":["Stony Brook University,Department of Electrical and Computer Engineering,Stony Brook,New York,USA"],"affiliations":[{"raw_affiliation_string":"Stony Brook University,Department of Electrical and Computer Engineering,Stony Brook,New York,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":["Stony Brook University,Department of Electrical and Computer Engineering,Stony Brook,New York,USA"],"affiliations":[{"raw_affiliation_string":"Stony Brook University,Department of Electrical and Computer Engineering,Stony Brook,New York,USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5037977097"],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15111082,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9775000214576721,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9775000214576721,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.954200029373169,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.9458000063896179,"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/outlier","display_name":"Outlier","score":0.7009093761444092},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6931466460227966},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6925840973854065},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6399015188217163},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6360955238342285},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5845977663993835},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5834237933158875},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4212530255317688},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3569340109825134},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3217032253742218},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29128772020339966},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.059668928384780884}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7009093761444092},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6931466460227966},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6925840973854065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6399015188217163},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6360955238342285},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5845977663993835},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5834237933158875},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4212530255317688},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3569340109825134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3217032253742218},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29128772020339966},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.059668928384780884},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/fusion59988.2024.10706404","is_oa":false,"landing_page_url":"https://doi.org/10.23919/fusion59988.2024.10706404","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 27th International Conference on Information Fusion (FUSION)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6299999952316284,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1579096296","https://openalex.org/W1999479532","https://openalex.org/W2067575867","https://openalex.org/W2091651498","https://openalex.org/W2116064496","https://openalex.org/W2139320579","https://openalex.org/W2143013621","https://openalex.org/W2143833711","https://openalex.org/W2564960719","https://openalex.org/W2620661538","https://openalex.org/W2912137037","https://openalex.org/W3043172702","https://openalex.org/W3204974012","https://openalex.org/W4211049957","https://openalex.org/W4211079158","https://openalex.org/W4211127940","https://openalex.org/W4221111879","https://openalex.org/W4384157306","https://openalex.org/W4392487745","https://openalex.org/W6609413351","https://openalex.org/W6798604401","https://openalex.org/W6809608413","https://openalex.org/W6866628794"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W2937099569","https://openalex.org/W3005992387","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660","https://openalex.org/W1964286703","https://openalex.org/W2169866437"],"abstract_inverted_index":{"Online":[0],"prediction":[1,40],"of":[2,24,35,41,52,87,122,134,142],"time":[3,42,69,113,120],"series":[4,43],"under":[5,44,170],"regime":[6,46],"switching":[7],"is":[8,55,136,163],"a":[9,33,76,140,147],"widely":[10],"studied":[11],"problem":[12],"in":[13],"the":[14,21,27,49,72,82,85,92,108,119,123,131],"literature,":[15],"with":[16,91,99,115,173],"many":[17],"celebrated":[18],"approaches.":[19],"Using":[20],"non-parametric":[22],"flexibility":[23],"Gaussian":[25],"processes,":[26],"recently":[28],"proposed":[29,161],"INTEL":[30,73,135,169],"algorithm":[31,74,125],"provides":[32],"product":[34],"experts":[36,143],"approach":[37,162],"to":[38,81,139],"online":[39],"possible":[45],"switching,":[47],"including":[48],"special":[50],"case":[51],"outliers.":[53],"This":[54],"achieved":[56],"by":[57],"adaptively":[58],"combining":[59],"several":[60],"candidate":[61],"models,":[62],"each":[63],"reporting":[64],"their":[65],"predictive":[66,83,101],"distribution":[67,111],"at":[68,112],"t.":[70],"However,":[71],"uses":[75,107],"finite":[77],"context":[78],"window":[79],"approximation":[80],"distribution,":[84],"computation":[86],"which":[88,106],"scales":[89,97],"cubically":[90],"maximum":[93],"lag,":[94],"or":[95],"otherwise":[96],"quartically":[98],"exact":[100,109],"distributions.":[102],"We":[103,127,156],"introduce":[104],"LINTEL,":[105],"filtering":[110],"t":[114],"constant-time":[116],"updates,":[117],"making":[118],"complexity":[121],"streaming":[124],"optimal.":[126],"additionally":[128],"note":[129],"that":[130,159],"weighting":[132],"mechanism":[133],"better":[137,174],"suited":[138],"mixture":[141],"approach,":[144],"and":[145],"propose":[146],"fusion":[148],"policy":[149],"based":[150],"on":[151],"arithmetic":[152],"averaging":[153],"for":[154],"LINTEL.":[155],"show":[157],"experimentally":[158],"our":[160],"over":[164],"five":[165],"times":[166],"faster":[167],"than":[168],"reasonable":[171],"settings":[172],"quality":[175],"predictions.":[176]},"counts_by_year":[],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}
