{"id":"https://openalex.org/W2913214128","doi":"https://doi.org/10.1109/bigdata.2018.8622585","title":"OPOSSAM: Online Prediction of Stream Data Using Self-adaptive Memory","display_name":"OPOSSAM: Online Prediction of Stream Data Using Self-adaptive Memory","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2913214128","doi":"https://doi.org/10.1109/bigdata.2018.8622585","mag":"2913214128"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8622585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","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/A5101773892","display_name":"Akihiro Yamaguchi","orcid":"https://orcid.org/0000-0002-6302-8989"},"institutions":[{"id":"https://openalex.org/I1292669757","display_name":"Toshiba (Japan)","ror":"https://ror.org/0326v3z14","country_code":"JP","type":"company","lineage":["https://openalex.org/I1292669757"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Akihiro Yamaguchi","raw_affiliation_strings":["System Engineering Lab, Toshiba Corporation, Japan"],"affiliations":[{"raw_affiliation_string":"System Engineering Lab, Toshiba Corporation, Japan","institution_ids":["https://openalex.org/I1292669757"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062677262","display_name":"Shigeru Maya","orcid":"https://orcid.org/0000-0002-5314-3203"},"institutions":[{"id":"https://openalex.org/I1292669757","display_name":"Toshiba (Japan)","ror":"https://ror.org/0326v3z14","country_code":"JP","type":"company","lineage":["https://openalex.org/I1292669757"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shigeru Maya","raw_affiliation_strings":["System Engineering Lab, Toshiba Corporation, Japan"],"affiliations":[{"raw_affiliation_string":"System Engineering Lab, Toshiba Corporation, Japan","institution_ids":["https://openalex.org/I1292669757"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033387408","display_name":"Tatsuya Inagi","orcid":null},"institutions":[{"id":"https://openalex.org/I1292669757","display_name":"Toshiba (Japan)","ror":"https://ror.org/0326v3z14","country_code":"JP","type":"company","lineage":["https://openalex.org/I1292669757"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tatsuya Inagi","raw_affiliation_strings":["System Engineering Lab, Toshiba Corporation, Japan"],"affiliations":[{"raw_affiliation_string":"System Engineering Lab, Toshiba Corporation, Japan","institution_ids":["https://openalex.org/I1292669757"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021269344","display_name":"Ken Ueno","orcid":"https://orcid.org/0000-0001-5580-2578"},"institutions":[{"id":"https://openalex.org/I1292669757","display_name":"Toshiba (Japan)","ror":"https://ror.org/0326v3z14","country_code":"JP","type":"company","lineage":["https://openalex.org/I1292669757"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ken Ueno","raw_affiliation_strings":["System Engineering Lab, Toshiba Corporation, Japan"],"affiliations":[{"raw_affiliation_string":"System Engineering Lab, Toshiba Corporation, Japan","institution_ids":["https://openalex.org/I1292669757"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101773892"],"corresponding_institution_ids":["https://openalex.org/I1292669757"],"apc_list":null,"apc_paid":null,"fwci":0.3258,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.69569933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"8226","issue":null,"first_page":"2355","last_page":"2364"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":1.0,"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":1.0,"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":0.9884999990463257,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9884999990463257,"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/computer-science","display_name":"Computer science","score":0.8166863918304443},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.7305852174758911},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.628790557384491},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5135736465454102},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.49746206402778625},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.4692901074886322},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44148629903793335},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.43856117129325867},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3617696166038513},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3125113844871521}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8166863918304443},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.7305852174758911},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.628790557384491},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5135736465454102},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.49746206402778625},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.4692901074886322},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44148629903793335},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.43856117129325867},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3617696166038513},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3125113844871521},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2018.8622585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8199999928474426}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W258831793","https://openalex.org/W1834088915","https://openalex.org/W1962414156","https://openalex.org/W1967681834","https://openalex.org/W1969574535","https://openalex.org/W1970833371","https://openalex.org/W1974961444","https://openalex.org/W1984969638","https://openalex.org/W1992171369","https://openalex.org/W2037051094","https://openalex.org/W2047332899","https://openalex.org/W2073427650","https://openalex.org/W2097908878","https://openalex.org/W2099302642","https://openalex.org/W2099419573","https://openalex.org/W2105103777","https://openalex.org/W2121541969","https://openalex.org/W2123565204","https://openalex.org/W2143991132","https://openalex.org/W2160943791","https://openalex.org/W2164793063","https://openalex.org/W2175433587","https://openalex.org/W2275526741","https://openalex.org/W2373739222","https://openalex.org/W2585508806","https://openalex.org/W2739567232","https://openalex.org/W2740558017","https://openalex.org/W3120740533","https://openalex.org/W6609721046","https://openalex.org/W6641016995","https://openalex.org/W6683438843","https://openalex.org/W6694655015"],"related_works":["https://openalex.org/W4307392573","https://openalex.org/W2802243998","https://openalex.org/W2736127210","https://openalex.org/W2329342202","https://openalex.org/W2574092225","https://openalex.org/W4200217704","https://openalex.org/W2161835057","https://openalex.org/W1521014365","https://openalex.org/W3208495060","https://openalex.org/W2740428142"],"abstract_inverted_index":{"There":[0],"is":[1,29],"a":[2],"need":[3],"for":[4,41],"forecasting":[5],"of":[6,66,117,123],"short-range":[7],"future":[8,57],"values":[9,58],"in":[10,25,49,103],"data":[11,27,43],"streams":[12,28],"such":[13,42],"as":[14],"traffic":[15,124],"flows,":[16],"stock":[17,126],"prices,":[18,127],"and":[19,52,55,84,128],"electricity":[20,129],"consumption.":[21,130],"However,":[22],"concept":[23,108],"drift":[24],"non-stationary":[26],"an":[30,35],"important":[31],"problem.":[32],"We":[33],"propose":[34],"online":[36],"prediction":[37,82,88],"method":[38],"called":[39],"OPOSSAM":[40,45,71],"streams.":[44],"manages":[46],"time-series":[47,67],"segments":[48],"short-term":[50,92],"memory":[51,74,93,102],"long-term":[53,73],"memory,":[54],"forecasts":[56],"by":[59,76],"local":[60],"regression":[61],"based":[62,90],"on":[63,91,120],"the":[64,87,95,100],"similarity":[65],"segments.":[68],"In":[69],"particular,":[70],"keeps":[72],"consistent":[75],"reducing":[77],"redundant":[78],"samples":[79],"with":[80,107],"large":[81],"errors,":[83],"automatically":[85],"adjusts":[86],"model":[89,97],"from":[94,99],"prior":[96],"learned":[98],"entire":[101],"order":[104],"to":[105,115],"deal":[106],"drift.":[109],"Experimental":[110],"results":[111],"show":[112],"accuracy":[113],"superior":[114],"that":[116],"baseline":[118],"methods":[119],"real-world":[121],"datasets":[122],"flow,":[125]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
