{"id":"https://openalex.org/W3008486058","doi":"https://doi.org/10.1109/bigdata47090.2019.9005511","title":"Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to Sequence Approach","display_name":"Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to Sequence Approach","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008486058","doi":"https://doi.org/10.1109/bigdata47090.2019.9005511","mag":"3008486058"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9005511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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/A5074615163","display_name":"Nikhil Muralidhar","orcid":"https://orcid.org/0000-0001-7068-2981"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nikhil Muralidhar","raw_affiliation_strings":["Computer Science Virginia Tech, Arlington, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Virginia Tech, Arlington, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037626731","display_name":"Sathappan Muthiah","orcid":null},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sathappan Muthiah","raw_affiliation_strings":["Computer Science Virginia Tech, Arlington, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Virginia Tech, Arlington, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087023609","display_name":"Kiyoshi Nakayama","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kiyoshi Nakayama","raw_affiliation_strings":["Energy Management NEC Labs America, San Jose, USA"],"affiliations":[{"raw_affiliation_string":"Energy Management NEC Labs America, San Jose, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102736348","display_name":"Ratnesh Sharma","orcid":"https://orcid.org/0000-0002-9139-2120"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ratnesh Sharma","raw_affiliation_strings":["Energy Management NEC Labs America, San Jose, USA"],"affiliations":[{"raw_affiliation_string":"Energy Management NEC Labs America, San Jose, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035052603","display_name":"Naren Ramakrishnan","orcid":"https://orcid.org/0000-0002-1821-9743"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naren Ramakrishnan","raw_affiliation_strings":["Computer Science Virginia Tech, Arlington, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Virginia Tech, Arlington, USA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5074615163"],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":0.5601,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.7637097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"543","last_page":"552"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9975000023841858,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9975000023841858,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9958000183105469,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9923999905586243,"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":0.762881338596344},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6588724851608276},{"id":"https://openalex.org/keywords/cyber-physical-system","display_name":"Cyber-physical system","score":0.5048545002937317},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5026683807373047},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4484347701072693},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4449236989021301},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4371604919433594},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4368327260017395},{"id":"https://openalex.org/keywords/notice","display_name":"Notice","score":0.4199989438056946},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4121946096420288},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.14430174231529236}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.762881338596344},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6588724851608276},{"id":"https://openalex.org/C179768478","wikidata":"https://www.wikidata.org/wiki/Q1120057","display_name":"Cyber-physical system","level":2,"score":0.5048545002937317},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5026683807373047},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4484347701072693},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4449236989021301},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4371604919433594},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4368327260017395},{"id":"https://openalex.org/C2779913896","wikidata":"https://www.wikidata.org/wiki/Q7063001","display_name":"Notice","level":2,"score":0.4199989438056946},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4121946096420288},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.14430174231529236},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9005511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W648786980","https://openalex.org/W854541894","https://openalex.org/W1489125746","https://openalex.org/W1810943226","https://openalex.org/W1902237438","https://openalex.org/W1924770834","https://openalex.org/W2064675550","https://openalex.org/W2073341130","https://openalex.org/W2107878631","https://openalex.org/W2117829824","https://openalex.org/W2127838378","https://openalex.org/W2130942839","https://openalex.org/W2131774270","https://openalex.org/W2133999440","https://openalex.org/W2143612262","https://openalex.org/W2149713648","https://openalex.org/W2157331557","https://openalex.org/W2176263492","https://openalex.org/W2178654303","https://openalex.org/W2415594836","https://openalex.org/W2467173223","https://openalex.org/W2470282262","https://openalex.org/W2474046725","https://openalex.org/W2530876040","https://openalex.org/W2540404261","https://openalex.org/W2583164363","https://openalex.org/W2608712415","https://openalex.org/W2613904329","https://openalex.org/W2753616590","https://openalex.org/W2962784628","https://openalex.org/W2963248296","https://openalex.org/W2963403868","https://openalex.org/W2963729263","https://openalex.org/W2963920996","https://openalex.org/W2963929190","https://openalex.org/W2963970666","https://openalex.org/W2964199361","https://openalex.org/W2964265128","https://openalex.org/W4293718132","https://openalex.org/W4297814361","https://openalex.org/W4301368689","https://openalex.org/W4385245566","https://openalex.org/W6621543089","https://openalex.org/W6623517193","https://openalex.org/W6629052376","https://openalex.org/W6638273328","https://openalex.org/W6638575559","https://openalex.org/W6640212811","https://openalex.org/W6679436768","https://openalex.org/W6680153409","https://openalex.org/W6685322675","https://openalex.org/W6685711979","https://openalex.org/W6716485080","https://openalex.org/W6720257300","https://openalex.org/W6720514713","https://openalex.org/W6729053843","https://openalex.org/W6732899423","https://openalex.org/W6737778391","https://openalex.org/W6739901393","https://openalex.org/W6744375652"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549","https://openalex.org/W3179858851","https://openalex.org/W3123177881"],"abstract_inverted_index":{"Cyber-physical":[0],"systems":[1],"(CPS)":[2],"are":[3,76,189],"ubiquitous":[4],"in":[5,84,166,215,246,263,279,291,306],"several":[6],"critical":[7],"infrastructure":[8],"applications.":[9],"Forecasting":[10,56],"the":[11,31,39,98,121,132,171,256,264,280,292],"state":[12,32,58,125],"of":[13,33,47,97,112,123,134,138,173,201,217,244,258,266,277,282,294],"CPS,":[14],"is":[15,27,102],"essential":[16],"for":[17,45,73,205,212,229,234],"better":[18,110,308],"planning,":[19],"resource":[20],"allocation":[21],"and":[22,53,68,79,108,127,130,145,178,197,209,269],"minimizing":[23],"operational":[24],"costs.":[25],"It":[26],"imperative":[28,103],"to":[29,41,50,64,104,150,191],"forecast":[30],"a":[34,62,135,161,288],"CPS":[35,48,124],"multiple":[36],"steps":[37],"into":[38],"future":[40],"afford":[42],"enough":[43],"time":[44],"planning":[46],"operation":[49],"minimize":[51],"costs":[52],"component":[54],"wear.":[55],"system":[57],"also":[59,169,221,254,300],"serves":[60],"as":[61,154,156,231,233],"precursor":[63],"detecting":[65],"process":[66],"anomalies":[67],"faults.":[69],"Concomitantly,":[70],"sensors":[71],"used":[72],"data":[74,100,106,128,235],"collection":[75],"commodity":[77],"hardware":[78],"experience":[80],"frequent":[81],"failures":[82],"resulting":[83],"periods":[85],"with":[86,160],"sparse":[87],"or":[88],"no":[89],"data.":[90],"In":[91,116],"such":[92],"cases,":[93],"re-construction":[94],"through":[95],"imputation":[96,129],"missing":[99],"sequences":[101],"alleviate":[105],"sparsity":[107],"enable":[109],"performance":[111,133,248,289],"down-stream":[113],"analytic":[114],"models.":[115],"this":[117],"paper,":[118],"we":[119,299],"tackle":[120],"problem":[122],"forecasting":[126,195,218,230,247,314],"characterize":[131],"wide":[136],"array":[137],"deep":[139],"learning":[140],"architectures":[141,153,158,208,214,268,285],"-":[142,159],"unidirectional":[143,251,283],"gated":[144,206],"non-gated":[146,213],"recurrent":[147,207],"architectures,":[148],"sequence":[149,151],"(Seq2Seq)":[152],"well":[155,232],"bidirectional":[157,224,295],"specific":[162],"focus":[163],"towards":[164],"applications":[165],"CPS.":[167],"We":[168,220,253],"study":[170],"impact":[172],"procedures":[174],"like":[175],"scheduled":[176,303],"sampling":[177,304],"attention,":[179],"on":[180],"model":[181],"training.":[182],"Our":[183],"results":[184],"indicate":[185],"that":[186,223,271,302,310],"Seq2Seq":[187,238,267,284,296],"models":[188,196,225,239,309],"superior":[190],"traditional":[192],"step":[193],"ahead":[194],"yield":[198,311],"an":[199,241,260,274],"improvement":[200,243,276],"at":[202],"least":[203],"28.5%":[204],"about":[210],"87.6%":[211],"terms":[216],"performance.":[219],"notice":[222],"learn":[226],"good":[227],"representations":[228],"imputation.":[236],"Bidirectional":[237],"show":[240],"average":[242,275],"17.6%":[245],"over":[249],"their":[250],"counterparts.":[252],"demonstrate":[255],"effect":[257],"employing":[259],"attention":[261],"mechanism":[262],"context":[265],"find":[270,301],"it":[272],"provides":[273],"57.12%":[278],"case":[281,293],"while":[286],"causing":[287],"decline":[290],"architectures.":[297],"Finally,":[298],"helps":[305],"training":[307],"significantly":[312],"lower":[313],"error.":[315]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}