{"id":"https://openalex.org/W3137044062","doi":"https://doi.org/10.1109/bigdata50022.2020.9378499","title":"FLOps: On Learning Important Time Series Features for Real-Valued Prediction","display_name":"FLOps: On Learning Important Time Series Features for Real-Valued Prediction","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3137044062","doi":"https://doi.org/10.1109/bigdata50022.2020.9378499","mag":"3137044062"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5033934770","display_name":"Dhaval Patel","orcid":"https://orcid.org/0000-0002-5449-6975"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dhaval Patel","raw_affiliation_strings":["IBM T. J. Watson Research Center, NY"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, NY","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085762525","display_name":"Syed Yousaf Shah","orcid":"https://orcid.org/0000-0003-1068-7312"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Syed Yousaf Shah","raw_affiliation_strings":["IBM T. J. Watson Research Center, NY"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, NY","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020632441","display_name":"Nianjun Zhou","orcid":"https://orcid.org/0000-0002-3473-6097"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nianjun Zhou","raw_affiliation_strings":["IBM T. J. Watson Research Center, NY"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, NY","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044958272","display_name":"Shrey Shrivastava","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shrey Shrivastava","raw_affiliation_strings":["IBM T. J. Watson Research Center, NY"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, NY","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102744844","display_name":"Arun Iyengar","orcid":"https://orcid.org/0000-0003-4679-1920"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arun Iyengar","raw_affiliation_strings":["IBM T. J. Watson Research Center, NY"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, NY","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039999123","display_name":"Anuradha Bhamidipaty","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anuradha Bhamidipaty","raw_affiliation_strings":["IBM T. J. Watson Research Center, NY"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, NY","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057936833","display_name":"Jayant Kalagnanam","orcid":"https://orcid.org/0009-0009-5051-2606"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jayant Kalagnanam","raw_affiliation_strings":["IBM T. J. Watson Research Center, NY"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, NY","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5033934770"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9092,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.75444371,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1624","last_page":"1633"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9997000098228455,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9973000288009644,"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"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9921000003814697,"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/computer-science","display_name":"Computer science","score":0.8110254406929016},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6470340490341187},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6459770202636719},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.635053813457489},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6276935935020447},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6246475577354431},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6003711819648743},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5182552337646484},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4914158582687378},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.48967936635017395},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.47273436188697815},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3588714003562927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8110254406929016},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6470340490341187},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6459770202636719},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.635053813457489},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6276935935020447},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6246475577354431},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6003711819648743},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5182552337646484},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4914158582687378},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.48967936635017395},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.47273436188697815},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3588714003562927},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1565746575","https://openalex.org/W1728842521","https://openalex.org/W1961115226","https://openalex.org/W2156665896","https://openalex.org/W2545001194","https://openalex.org/W2555077524","https://openalex.org/W2604847698","https://openalex.org/W2605940696","https://openalex.org/W2794778778","https://openalex.org/W2802314367","https://openalex.org/W2943593920","https://openalex.org/W2950050081","https://openalex.org/W2963440486","https://openalex.org/W2966284335","https://openalex.org/W2966607134","https://openalex.org/W2967988901","https://openalex.org/W2971724044","https://openalex.org/W2972537982","https://openalex.org/W2974580236","https://openalex.org/W2989055432","https://openalex.org/W3012919764","https://openalex.org/W3014052783","https://openalex.org/W3036474237","https://openalex.org/W3036493572","https://openalex.org/W3042807565","https://openalex.org/W3101898419","https://openalex.org/W3191026187","https://openalex.org/W6633774736","https://openalex.org/W6637572315","https://openalex.org/W6729149246","https://openalex.org/W6762262900","https://openalex.org/W6766596047","https://openalex.org/W6767885493","https://openalex.org/W6771047299","https://openalex.org/W6775255555","https://openalex.org/W6779718140","https://openalex.org/W6780026185","https://openalex.org/W6780415234","https://openalex.org/W6800239483"],"related_works":["https://openalex.org/W3037187668","https://openalex.org/W4234772502","https://openalex.org/W2380685755","https://openalex.org/W2252100032","https://openalex.org/W2963436428","https://openalex.org/W4400978025","https://openalex.org/W2918743509","https://openalex.org/W2734796617","https://openalex.org/W3083218341","https://openalex.org/W3034087822"],"abstract_inverted_index":{"Time":[0,18],"series":[1,10,19,43,66,101],"value":[2],"forecasting":[3],"using":[4,110,121,127,151],"machine":[5,169],"learning":[6,28,170],"models":[7,171],"utilizing":[8],"time":[9,42,65,100],"features":[11,35,40,56,78,98,107,118,137],"has":[12],"recently":[13],"got":[14],"good":[15],"attention":[16],"of":[17,38,75,87,145,157,168],"analytics":[20],"community.":[21],"This":[22],"paper":[23,47],"proposes":[24,49],"an":[25],"automated":[26],"feature":[27,129,162,178],"mechanisms":[29,70],"to":[30,53,173,184],"filter":[31,55],"out":[32],"most":[33,59,76],"useful":[34,77],"from":[36],"hundreds":[37],"available":[39],"for":[41,61,79,99],"prediction":[44,89],"problems.":[45],"The":[46,135],"further":[48],"a":[50,122],"novel":[51],"mechanism":[52,93],"dynamically":[54],"that":[57,124,154],"are":[58,116,119,138],"suitable":[60],"the":[62,85,88,106,143,146,165,174],"given":[63,80],"input":[64,81,147,158],"data.":[67],"With":[68],"such":[69],"we":[71],"create":[72],"pipeline":[73],"consisting":[74],"data":[82,159],"and":[83,104,131],"increases":[84],"performance":[86,167],"model.":[90],"Our":[91],"proposed":[92],"first,":[94],"groups":[95],"well":[96],"known":[97],"analysis,":[102],"generates":[103],"assigns":[105],"importance":[108],"score":[109,130],"multiple":[111],"scoring":[112],"configurations.":[113],"Once":[114],"scores":[115],"assigned,":[117],"filtered":[120,136],"threshold":[123],"is":[125,180],"derived":[126],"reference":[128],"Critical":[132],"Difference":[133],"diagram.":[134],"subsequently":[139],"analyzed":[140],"based":[141,160],"on":[142],"characteristics":[144],"dataset.":[148],"We":[149],"show":[150],"experimental":[152],"results":[153],"our":[155],"approach":[156],"dynamic":[161,177],"selection":[163],"improves":[164],"overall":[166],"compared":[172],"case":[175],"where":[176],"extraction":[179],"not":[181],"applied":[182],"prior":[183],"modeling.":[185]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
