{"id":"https://openalex.org/W4308090255","doi":"https://doi.org/10.1109/hpec55821.2022.9926406","title":"An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning","display_name":"An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning","publication_year":2022,"publication_date":"2022-09-19","ids":{"openalex":"https://openalex.org/W4308090255","doi":"https://doi.org/10.1109/hpec55821.2022.9926406"},"language":"en","primary_location":{"id":"doi:10.1109/hpec55821.2022.9926406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec55821.2022.9926406","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE High Performance Extreme Computing Conference (HPEC)","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/A5087765289","display_name":"Matthew L. Weiss","orcid":"https://orcid.org/0000-0003-2930-1520"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Matthew L. Weiss","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004090802","display_name":"Joseph McDonald","orcid":"https://orcid.org/0009-0004-6477-8476"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph McDonald","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072368385","display_name":"David Bestor","orcid":"https://orcid.org/0009-0002-7684-1191"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Bestor","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110423798","display_name":"Charles Yee","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charles Yee","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037735778","display_name":"Daniel Edelman","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109586","display_name":"Moscow Institute of Thermal Technology","ror":"https://ror.org/021es5e59","country_code":"RU","type":"facility","lineage":["https://openalex.org/I4210109586"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Daniel Edelman","raw_affiliation_strings":["MIT"],"affiliations":[{"raw_affiliation_string":"MIT","institution_ids":["https://openalex.org/I4210109586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064275902","display_name":"Michael Jones","orcid":"https://orcid.org/0000-0001-5215-2346"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Jones","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103257220","display_name":"Andrew Prout","orcid":"https://orcid.org/0000-0002-4408-0247"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew Prout","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008297036","display_name":"Andrew Bowne","orcid":null},"institutions":[{"id":"https://openalex.org/I4210089612","display_name":"United States Air Force","ror":"https://ror.org/006gmme17","country_code":"US","type":"funder","lineage":["https://openalex.org/I1330347796","https://openalex.org/I4210089612","https://openalex.org/I4210102105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew Bowne","raw_affiliation_strings":["US Air Force"],"affiliations":[{"raw_affiliation_string":"US Air Force","institution_ids":["https://openalex.org/I4210089612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000231892","display_name":"Lindsey McEvoy","orcid":null},"institutions":[{"id":"https://openalex.org/I4210089612","display_name":"United States Air Force","ror":"https://ror.org/006gmme17","country_code":"US","type":"funder","lineage":["https://openalex.org/I1330347796","https://openalex.org/I4210089612","https://openalex.org/I4210102105"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lindsey McEvoy","raw_affiliation_strings":["US Air Force"],"affiliations":[{"raw_affiliation_string":"US Air Force","institution_ids":["https://openalex.org/I4210089612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043450560","display_name":"Vijay Gadepally","orcid":"https://orcid.org/0000-0002-4598-2808"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vijay Gadepally","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103227438","display_name":"Siddharth Samsi","orcid":"https://orcid.org/0009-0000-2884-9688"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siddharth Samsi","raw_affiliation_strings":["MIT Lincoln Laboratory"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory","institution_ids":["https://openalex.org/I4210122954"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":11,"corresponding_author_ids":["https://openalex.org/A5087765289"],"corresponding_institution_ids":["https://openalex.org/I4210122954"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12438901,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"31","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9983000159263611,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9961000084877014,"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":0.8160394430160522},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.7096611261367798},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5849059820175171},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5770282745361328},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5759914517402649},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5601027607917786},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.53987717628479},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5148876905441284},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4996659755706787},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.48469188809394836},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4631910026073456},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.4420159161090851}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8160394430160522},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.7096611261367798},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5849059820175171},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5770282745361328},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5759914517402649},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5601027607917786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.53987717628479},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5148876905441284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4996659755706787},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.48469188809394836},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4631910026073456},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.4420159161090851},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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:10.1109/hpec55821.2022.9926406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec55821.2022.9926406","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W642667799","https://openalex.org/W658512522","https://openalex.org/W1930624869","https://openalex.org/W2101234009","https://openalex.org/W2108482774","https://openalex.org/W2128160875","https://openalex.org/W2295124130","https://openalex.org/W2396223002","https://openalex.org/W2787894218","https://openalex.org/W2890141553","https://openalex.org/W2910237859","https://openalex.org/W2911487170","https://openalex.org/W2911964244","https://openalex.org/W2914065240","https://openalex.org/W2969296454","https://openalex.org/W3205978767","https://openalex.org/W4205140476","https://openalex.org/W4239510810","https://openalex.org/W4289827966","https://openalex.org/W4308001759","https://openalex.org/W6675354045","https://openalex.org/W6676342998","https://openalex.org/W6734312481","https://openalex.org/W6754447547","https://openalex.org/W6759367172","https://openalex.org/W6773003515","https://openalex.org/W6779758576","https://openalex.org/W6802566335"],"related_works":["https://openalex.org/W2000785801","https://openalex.org/W986318368","https://openalex.org/W2384410913","https://openalex.org/W2352878646","https://openalex.org/W2004734601","https://openalex.org/W2130149817","https://openalex.org/W2990194547","https://openalex.org/W1480123525","https://openalex.org/W2948549837","https://openalex.org/W1566614651"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3,19,114],"address":[4,115],"the":[5,23,63,110,116,153,176,192,221],"application":[6],"of":[7,32,42,80,146,208],"pre-processing":[8],"techniques":[9],"to":[10,21,92,152,170,190,204,219],"multi-channel":[11,33,57,171],"time":[12,34,58,132,140,149,172],"series":[13,35,59,150,173],"data":[14,36,60],"with":[15,109,175,197],"varying":[16,48,77],"lengths,":[17],"which":[18],"refer":[20],"as":[22,45,99,213,225],"alignment":[24,117,193,222],"problem,":[25,194,223],"for":[26,39,96,216],"downstream":[27],"machine":[28,199],"learning.":[29],"The":[30],"misalignment":[31,88],"may":[37],"occur":[38],"a":[40,126,130,138,144,161,214],"variety":[41],"reasons,":[43],"such":[44,98,224],"missing":[46],"data,":[47],"sampling":[49,125,143],"rates,":[50],"or":[51],"inconsistent":[52],"collection":[53],"times.":[54],"We":[55],"consider":[56],"collected":[61],"from":[62,129,148],"MIT":[64,111,177],"SuperCloud":[65,112,178],"High":[66],"Performance":[67],"Computing":[68],"(HPC)":[69],"center,":[70],"where":[71],"different":[72],"job":[73],"start":[74],"times":[75,79],"and":[76,142,211],"run":[78],"HPC":[81],"jobs":[82],"result":[83],"in":[84,195],"misaligned":[85],"data.":[86],"This":[87],"makes":[89],"it":[90],"challenging":[91],"build":[93],"AI/ML":[94],"approaches":[95,169,189,218],"tasks":[97],"compute":[100],"workload":[101],"classification.":[102],"Building":[103],"on":[104,137],"previous":[105,168],"supervised":[106],"classification":[107,162,174,209],"work":[108],"Dataset,":[113],"problem":[118],"via":[119],"three":[120],"broad,":[121],"low":[122,187],"overhead":[123,188],"approaches:":[124],"fixed":[127],"subset":[128,145],"full":[131,139],"series,":[133,141],"performing":[134,158],"summary":[135],"statistics":[136],"coefficients":[147],"mapped":[151],"frequency":[154],"domain.":[155],"Our":[156],"best":[157],"models":[159],"achieve":[160,205],"accuracy":[163],"greater":[164],"than":[165],"95%,":[166],"outperforming":[167],"Dataset":[179],"by":[180],"5":[181],"%.":[182],"These":[183],"results":[184],"indicate":[185],"our":[186],"solving":[191],"conjunction":[196],"standard":[198],"learning":[200],"techniques,":[201],"are":[202],"able":[203],"high":[206],"levels":[207],"accuracy,":[210],"serve":[212],"baseline":[215],"future":[217],"addressing":[220],"kernel":[226],"methods.":[227]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
