{"id":"https://openalex.org/W2735282772","doi":"https://doi.org/10.23919/acc.2017.7963431","title":"Probabilistic forecasting of symbol sequences with deep neural networks","display_name":"Probabilistic forecasting of symbol sequences with deep neural networks","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735282772","doi":"https://doi.org/10.23919/acc.2017.7963431","mag":"2735282772"},"language":"en","primary_location":{"id":"doi:10.23919/acc.2017.7963431","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc.2017.7963431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 American Control Conference (ACC)","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/A5018102423","display_name":"Michael Hauser","orcid":"https://orcid.org/0000-0003-3816-9218"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Michael Hauser","raw_affiliation_strings":["Department of Mechanical and Nuclear Engineering, Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Nuclear Engineering, Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107923354","display_name":"Yiwei Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiwei Fu","raw_affiliation_strings":["Department of Mechanical and Nuclear Engineering, Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Nuclear Engineering, Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100733780","display_name":"Yue Li","orcid":"https://orcid.org/0000-0002-7334-3712"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yue Li","raw_affiliation_strings":["Department of Mechanical and Nuclear Engineering, Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Nuclear Engineering, Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041594244","display_name":"Shashi Phoha","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shashi Phoha","raw_affiliation_strings":["Shashi Phoha is with the Applied Research Laboratory, Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"Shashi Phoha is with the Applied Research Laboratory, Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003487011","display_name":"Asok Ray","orcid":"https://orcid.org/0000-0003-4124-0230"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Asok Ray","raw_affiliation_strings":["Department of Mechanical and Nuclear Engineering, Pennsylvania State University"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Nuclear Engineering, Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5018102423"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":0.2401,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.53100836,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"3147","last_page":"3152"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10553","display_name":"Combustion and flame dynamics","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10553","display_name":"Combustion and flame dynamics","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9603999853134155,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9592999815940857,"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/autoregressive-model","display_name":"Autoregressive model","score":0.7131505608558655},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7039391398429871},{"id":"https://openalex.org/keywords/autoregressive\u2013moving-average-model","display_name":"Autoregressive\u2013moving-average model","score":0.5553681254386902},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5377286672592163},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.5339008569717407},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5224775075912476},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5168481469154358},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.46932002902030945},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4549197852611542},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.44332998991012573},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38359305262565613},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3638133108615875},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32998502254486084},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2864968776702881},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27977466583251953}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7131505608558655},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7039391398429871},{"id":"https://openalex.org/C74883015","wikidata":"https://www.wikidata.org/wiki/Q290467","display_name":"Autoregressive\u2013moving-average model","level":3,"score":0.5553681254386902},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5377286672592163},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.5339008569717407},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5224775075912476},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5168481469154358},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.46932002902030945},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4549197852611542},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.44332998991012573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38359305262565613},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3638133108615875},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32998502254486084},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2864968776702881},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27977466583251953},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/acc.2017.7963431","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc.2017.7963431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 American Control Conference (ACC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W44815768","https://openalex.org/W830721899","https://openalex.org/W1522237958","https://openalex.org/W1586335931","https://openalex.org/W1592642180","https://openalex.org/W1822742915","https://openalex.org/W2001179000","https://openalex.org/W2030305104","https://openalex.org/W2114655358","https://openalex.org/W2116064496","https://openalex.org/W2129783285","https://openalex.org/W2136922672","https://openalex.org/W2137983211","https://openalex.org/W2169739344","https://openalex.org/W2181523240","https://openalex.org/W2210387432","https://openalex.org/W2384495648","https://openalex.org/W2482115077","https://openalex.org/W2798058877","https://openalex.org/W2919115771","https://openalex.org/W4213041519","https://openalex.org/W4231109964","https://openalex.org/W4253175466","https://openalex.org/W6601785968","https://openalex.org/W6638364407"],"related_works":["https://openalex.org/W199725827","https://openalex.org/W4248896451","https://openalex.org/W4394470125","https://openalex.org/W1983856919","https://openalex.org/W2349019353","https://openalex.org/W3002873285","https://openalex.org/W2185837235","https://openalex.org/W3097417252","https://openalex.org/W1989764538","https://openalex.org/W2021955905"],"abstract_inverted_index":{"Time":[0],"series":[1,61],"forecasting":[2,58,112,155],"is":[3,21,84,109,161],"usually":[4],"done":[5],"in":[6,12,35,169],"a":[7,18,24,36,44,52,74,130,150],"deterministic":[8],"sense,":[9,38],"such":[10],"as":[11,23,149],"autoregressive":[13,142],"moving":[14,143],"average":[15,144],"models,":[16],"where":[17],"future":[19,71],"state":[20],"predicted":[22],"linear":[25],"combination":[26],"of":[27,59,67,70,79,82,105,115,125],"past":[28],"events.":[29],"However,":[30],"by":[31,62,111],"formulating":[32],"the":[33,64,68,91,106,113,121,126,141,158,166],"problem":[34],"probabilistic":[37,57,88],"soft":[39],"predictions":[40],"are":[41],"obtained":[42],"from":[43,73,90],"given":[45,75],"probability":[46,69],"mass":[47],"function.":[48],"This":[49],"paper":[50],"uses":[51],"deep":[53],"neural":[54],"network":[55],"for":[56,152],"time":[60],"minimizing":[63],"cross":[65],"entropy":[66],"symbols":[72],"state.":[76],"The":[77,103,134],"advantage":[78],"this":[80,170],"type":[81],"model":[83,108,160,168],"that":[85],"it":[86],"makes":[87],"inferences":[89],"ground":[92],"up,":[93],"and":[94,157],"without":[95],"any":[96],"restrictive":[97],"assumptions":[98],"(e.g.,":[99],"second":[100],"order":[101],"statistics).":[102],"efficacy":[104],"proposed":[107,135,159],"tested":[110],"emergence":[114],"combustion":[116],"instabilities,":[117],"defined":[118],"to":[119,163],"be":[120],"root":[122],"mean":[123],"square":[124],"pressure":[127],"signal":[128],"inside":[129],"laboratory-scale":[131],"combustor":[132],"system.":[133],"algorithm":[136],"has":[137],"been":[138],"compared":[139],"with":[140],"(ARMA)":[145],"model,":[146],"which":[147],"acts":[148],"baseline":[151],"many":[153],"time-series":[154],"tasks,":[156],"shown":[162],"significantly":[164],"outperform":[165],"ARMA":[167],"task.":[171]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}