{"id":"https://openalex.org/W1917205115","doi":"https://doi.org/10.1109/cifer.1997.618944","title":"Taking time seriously: hidden Markov experts applied to financial engineering","display_name":"Taking time seriously: hidden Markov experts applied to financial engineering","publication_year":2002,"publication_date":"2002-11-22","ids":{"openalex":"https://openalex.org/W1917205115","doi":"https://doi.org/10.1109/cifer.1997.618944","mag":"1917205115"},"language":"en","primary_location":{"id":"doi:10.1109/cifer.1997.618944","doi":"https://doi.org/10.1109/cifer.1997.618944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cifer.1997.618944","pdf_url":null,"source":{"id":"https://openalex.org/S4306422520","display_name":"IEEE Conference on Computational Intelligence for Financial Engineering & Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5111929193","display_name":"Shanming Shi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I188538660","display_name":"University of Colorado Boulder","ror":"https://ror.org/02ttsq026","country_code":"US","type":"education","lineage":["https://openalex.org/I188538660"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shanming Shi","raw_affiliation_strings":["Department of Computer Science, University of Colorado, Boulder, CO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Colorado, Boulder, CO, USA","institution_ids":["https://openalex.org/I188538660"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037027451","display_name":"Andreas S. Weigend","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A.S. Weigend","raw_affiliation_strings":["Department of Information Systems, Leonard N. Stern School of Business., New York University, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Systems, Leonard N. Stern School of Business., New York University, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.0537,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.91577753,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":"3","issue":null,"first_page":"244","last_page":"252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9375,"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"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9375,"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/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.025499999523162842,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.019700000062584877,"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/hidden-markov-models","display_name":"hidden Markov models","score":0.9950000047683716},{"id":"https://openalex.org/keywords/time-series-modeling","display_name":"time series modeling","score":0.7160000205039978},{"id":"https://openalex.org/keywords/time-dependence","display_name":"time dependence","score":0.6269999742507935},{"id":"https://openalex.org/keywords/hidden-states","display_name":"hidden states","score":0.7239999771118164},{"id":"https://openalex.org/keywords/financial-time-series","display_name":"financial time series","score":0.4950000047683716},{"id":"https://openalex.org/keywords/s-p-500","display_name":"S&P 500","score":0.890999972820282},{"id":"https://openalex.org/keywords/exchange-rates","display_name":"exchange rates","score":0.8450000286102295},{"id":"https://openalex.org/keywords/volatility","display_name":"volatility","score":0.6069999933242798},{"id":"https://openalex.org/keywords/high-frequency-data","display_name":"high-frequency data","score":0.6029999852180481},{"id":"https://openalex.org/keywords/financial-engineering","display_name":"financial engineering","score":0.3889999985694885}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.8725715279579163},{"id":"https://openalex.org/C64939953","wikidata":"https://www.wikidata.org/wiki/Q3859882","display_name":"Hidden semi-Markov model","level":5,"score":0.8496227264404297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6575899720191956},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.6303954124450684},{"id":"https://openalex.org/C196956702","wikidata":"https://www.wikidata.org/wiki/Q6795829","display_name":"Maximum-entropy Markov model","level":5,"score":0.6153619289398193},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.605707049369812},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5910155177116394},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5668911933898926},{"id":"https://openalex.org/C189973286","wikidata":"https://www.wikidata.org/wiki/Q176695","display_name":"Markov property","level":4,"score":0.4865099787712097},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4685180187225342},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.457692950963974},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4323943257331848},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.41365641355514526},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35866254568099976},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3311423659324646},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24465525150299072},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15155336260795593},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cifer.1997.618944","doi":"https://doi.org/10.1109/cifer.1997.618944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cifer.1997.618944","pdf_url":null,"source":{"id":"https://openalex.org/S4306422520","display_name":"IEEE Conference on Computational Intelligence for Financial Engineering & Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1575431606","https://openalex.org/W2009225465","https://openalex.org/W2052441401","https://openalex.org/W2061358456","https://openalex.org/W2125838338","https://openalex.org/W2150884987","https://openalex.org/W2165959773","https://openalex.org/W4241115065"],"related_works":["https://openalex.org/W2247164508","https://openalex.org/W7100063602","https://openalex.org/W2541774717","https://openalex.org/W2054757694","https://openalex.org/W7099488143","https://openalex.org/W7073979723","https://openalex.org/W2127270646","https://openalex.org/W3159220025","https://openalex.org/W191130283","https://openalex.org/W2408022934"],"abstract_inverted_index":{"Most":[0],"traditional":[1],"time":[2,11,56,92,95,179],"series":[3,96,180],"models":[4,7,75,113],"are":[5,39,114],"global":[6,173],"based":[8,71],"on":[9,72,128],"local":[10,112],"information:":[12],"they":[13,64],"assume":[14],"that":[15,63],"the":[16,51,83,118,129,135,139,169,178,185],"state":[17],"can":[18,122,181],"be":[19,123,182],"fully":[20],"and":[21,138,153,171],"locally":[22],"(in":[23],"time)":[24],"characterized":[25,183],"with":[26,103],"a":[27,55,99],"finite":[28],"embedding":[29],"space.":[30],"Prediction":[31],"then":[32,146],"amounts":[33],"to":[34,49,116,149,154],"simple":[35,44],"regression.":[36],"Unfortunately,":[37],"there":[38],"many":[40],"situations":[41],"in":[42,54,77],"which":[43,121],"regression":[45],"is":[46,70],"not":[47],"sufficient":[48],"model":[50,91,102],"temporal":[52],"structure":[53],"series.":[57],"The":[58,131,161,175],"authors":[59],"introduce":[60],"an":[61],"architecture":[62],"call":[65],"hidden":[66,73,86,88,107,143,162,186],"Markov":[67,74,89,101,144,163],"experts.":[68],"It":[69],"used":[76],"speech":[78],"recognition":[79],"research.":[80],"By":[81],"introducing":[82],"concept":[84],"of":[85,94,142,177],"states,":[87],"experts":[90,164],"dependency":[93],"explicitly":[97],"as":[98],"first-order":[100],"transitions":[104],"between":[105],"these":[106],"states.":[108,187],"Within":[109],"each":[110],"state,":[111],"applied":[115],"estimate":[117],"probability":[119],"density,":[120],"linear":[124,170],"or":[125],"nonlinear":[126,172],"depending":[127],"situation.":[130],"paper":[132],"first":[133],"discusses":[134],"statistical":[136],"framework":[137],"learning":[140],"algorithm":[141],"experts,":[145],"applies":[147],"them":[148],"daily":[150],"S&P500":[151],"data":[152],"high":[155],"frequency":[156],"currency":[157],"exchange":[158],"rate":[159],"data.":[160],"have":[165],"better":[166],"profit":[167],"than":[168],"models.":[174],"volatilities":[176],"by":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":3}],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2016-06-24T00:00:00"}
