{"id":"https://openalex.org/W4402675014","doi":"https://doi.org/10.1063/5.0234782","title":"Implementation of Gaussian hidden Markov model (GHMM) to predict gold prices in Indonesia","display_name":"Implementation of Gaussian hidden Markov model (GHMM) to predict gold prices in Indonesia","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4402675014","doi":"https://doi.org/10.1063/5.0234782"},"language":"en","primary_location":{"id":"doi:10.1063/5.0234782","doi":"https://doi.org/10.1063/5.0234782","is_oa":false,"landing_page_url":"https://doi.org/10.1063/5.0234782","pdf_url":null,"source":{"id":"https://openalex.org/S2764696622","display_name":"AIP conference proceedings","issn_l":"0094-243X","issn":["0094-243X","1551-7616","1935-0465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310320257","host_organization_name":"American Institute of Physics","host_organization_lineage":["https://openalex.org/P4310320257"],"host_organization_lineage_names":["American Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AIP Conference Proceedings","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/A5107489340","display_name":"Ayu Windah Setyowati","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102893293","display_name":"State University of Malang","ror":"https://ror.org/00ypgyy34","country_code":"ID","type":"education","lineage":["https://openalex.org/I102893293"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Ayu Windah Setyowati","raw_affiliation_strings":["Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Malang, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Malang, Indonesia","institution_ids":["https://openalex.org/I102893293"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089719517","display_name":"Trianingsih Eni Lestari","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102893293","display_name":"State University of Malang","ror":"https://ror.org/00ypgyy34","country_code":"ID","type":"education","lineage":["https://openalex.org/I102893293"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Trianingsih Eni Lestari","raw_affiliation_strings":["Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Malang, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Malang, Indonesia","institution_ids":["https://openalex.org/I102893293"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5089719517"],"corresponding_institution_ids":["https://openalex.org/I102893293"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2081537,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3235","issue":null,"first_page":"020025","last_page":"020025"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9990000128746033,"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":0.9990000128746033,"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":0.00039999998989515007,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/gaussian-hidden-markov-model","display_name":"Gaussian hidden Markov model","score":0.9769999980926514},{"id":"https://openalex.org/keywords/gold-price-forecasting","display_name":"gold price forecasting","score":0.9810000061988831},{"id":"https://openalex.org/keywords/aic-and-bic","display_name":"AIC and BIC","score":0.7789999842643738},{"id":"https://openalex.org/keywords/baum-welch-algorithm","display_name":"Baum-Welch algorithm","score":0.9950000047683716},{"id":"https://openalex.org/keywords/short-term-price-forecasting","display_name":"short-term price forecasting","score":0.6570000052452087},{"id":"https://openalex.org/keywords/financial-time-series","display_name":"financial time series","score":0.6399999856948853}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.720116138458252},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5558058619499207},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.5047940015792847},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4817109704017639},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4554942548274994},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4325811266899109},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4004569351673126},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.37441158294677734},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2972358465194702},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2023586630821228},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1063/5.0234782","doi":"https://doi.org/10.1063/5.0234782","is_oa":false,"landing_page_url":"https://doi.org/10.1063/5.0234782","pdf_url":null,"source":{"id":"https://openalex.org/S2764696622","display_name":"AIP conference proceedings","issn_l":"0094-243X","issn":["0094-243X","1551-7616","1935-0465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310320257","host_organization_name":"American Institute of Physics","host_organization_lineage":["https://openalex.org/P4310320257"],"host_organization_lineage_names":["American Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AIP Conference Proceedings","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":17,"referenced_works":["https://openalex.org/W65445265","https://openalex.org/W1486632395","https://openalex.org/W1550934241","https://openalex.org/W1568339100","https://openalex.org/W2001083955","https://openalex.org/W2005096544","https://openalex.org/W2007321142","https://openalex.org/W2065604482","https://openalex.org/W2125838338","https://openalex.org/W2169533279","https://openalex.org/W2170125064","https://openalex.org/W2438452208","https://openalex.org/W2610593146","https://openalex.org/W2770406862","https://openalex.org/W2922530236","https://openalex.org/W3020201552","https://openalex.org/W4391914491"],"related_works":["https://openalex.org/W3022932314","https://openalex.org/W3138473290","https://openalex.org/W7217970317","https://openalex.org/W7196932810","https://openalex.org/W4390606912","https://openalex.org/W2101490097","https://openalex.org/W7061883390","https://openalex.org/W4401349315","https://openalex.org/W1612818313","https://openalex.org/W4406634675"],"abstract_inverted_index":{"The":[0,62,95,118],"Gaussian":[1,15],"Hidden":[2,8],"Markov":[3,9],"Model":[4,10],"(GHMM)":[5],"is":[6,27,39,48,57,65,98,142,177],"a":[7,155,166],"(HMM)":[11],"that":[12,124,179],"uses":[13],"the":[14,21,49,68,74,83,88,92,101,112,115,121,125,131,136,146,149,161],"Distribution":[16,19],"or":[17,171],"Normal":[18],"as":[20],"probability":[22],"of":[23,71,103,120,128,148,158,169],"its":[24],"observations.":[25],"GHMM":[26,38,180],"an":[28],"effective":[29],"method":[30],"used":[31,40],"in":[32,45,59,82,105,185],"various":[33],"fields.":[34],"In":[35],"this":[36],"study,":[37],"to":[41,66,99],"predict":[42,100,182],"gold":[43,104,183],"prices":[44,184],"Indonesia,":[46],"which":[47],"best":[50],"asset":[51],"for":[52,107],"investment.":[53],"Gold":[54],"price":[55,102],"prediction":[56,162],"done":[58],"two":[60],"stages.":[61],"first":[63],"stage":[64,97],"determine":[67],"optimum":[69,126],"number":[70,127],"states,":[72],"where":[73],"smallest":[75,137],"AIC":[76,138],"and":[77,86,139],"BIC":[78,140],"values":[79,90,141,168],"are":[80],"selected":[81],"training":[84,132],"data":[85,93,133,152],"evaluate":[87],"MAPE":[89,156,167],"on":[91,135],"testing.":[94],"second":[96],"Indonesia":[106,186],"ten":[108],"days":[109],"by":[110],"calibrating":[111],"parameters":[113],"using":[114],"Baum-Welch":[116],"Algorithm.":[117],"results":[119,163],"study":[122],"show":[123],"states":[129],"from":[130],"based":[134],"5":[143],"(five).":[144],"Then":[145,160],"evaluation":[147],"model":[150],"against":[151],"testing":[153],"obtained":[154,164],"value":[157],"0,55%.":[159],"have":[165],"0,30%":[170],"less":[172],"than":[173],"10%.":[174],"Therefore,":[175],"it":[176],"concluded":[178],"can":[181],"well.":[187]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2024-09-21T00:00:00"}