{"id":"https://openalex.org/W4411887252","doi":"https://doi.org/10.54364/aaiml.2025.52216","title":"Assessing Lag-Llama in Probabilistic Time Series Forecasting for the Indonesian Stock Market","display_name":"Assessing Lag-Llama in Probabilistic Time Series Forecasting for the Indonesian Stock Market","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4411887252","doi":"https://doi.org/10.54364/aaiml.2025.52216"},"language":"en","primary_location":{"id":"doi:10.54364/aaiml.2025.52216","is_oa":true,"landing_page_url":"https://doi.org/10.54364/aaiml.2025.52216","pdf_url":"https://doi.org/10.54364/aaiml.2025.52216","source":{"id":"https://openalex.org/S4210238872","display_name":"Advances in Artificial Intelligence and Machine Learning","issn_l":"2582-9793","issn":["2582-9793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Artificial Intelligence and Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.54364/aaiml.2025.52216","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088166165","display_name":"Arbi Haza Nasution","orcid":"https://orcid.org/0000-0001-6283-3217"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Arbi Haza Nasution","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028430689","display_name":"Anggi Hanafiah","orcid":"https://orcid.org/0000-0001-9102-7089"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anggi Hanafiah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000256030","display_name":"Winda Monika","orcid":"https://orcid.org/0000-0002-4637-5735"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Winda Monika","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044659591","display_name":"Rajalingam Sokkalingam","orcid":"https://orcid.org/0000-0002-3747-8695"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rajalingam Sokkalingam","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112342071","display_name":"Mohd Sham Mohamad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohd Sham Mohamad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5059994136","display_name":"Andry Alamsyah","orcid":"https://orcid.org/0000-0001-5106-7561"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andry Alamsyah","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5088166165"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2022,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.88613247,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"05","issue":"02","first_page":"3809","last_page":"3833"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9736999869346619,"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":0.9736999869346619,"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":0.9592000246047974,"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/indonesian","display_name":"Indonesian","score":0.779446005821228},{"id":"https://openalex.org/keywords/lag","display_name":"Lag","score":0.5997081398963928},{"id":"https://openalex.org/keywords/time-lag","display_name":"Time lag","score":0.5712607502937317},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5313169360160828},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5219725370407104},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5145620703697205},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5104312300682068},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.45730215311050415},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3551746904850006},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35182762145996094},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.2969836890697479},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2813602685928345},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.2098883092403412},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.06066533923149109},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.04850566387176514}],"concepts":[{"id":"https://openalex.org/C2779207338","wikidata":"https://www.wikidata.org/wiki/Q9240","display_name":"Indonesian","level":2,"score":0.779446005821228},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.5997081398963928},{"id":"https://openalex.org/C2987767877","wikidata":"https://www.wikidata.org/wiki/Q352862","display_name":"Time lag","level":3,"score":0.5712607502937317},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5313169360160828},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5219725370407104},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5145620703697205},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5104312300682068},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.45730215311050415},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3551746904850006},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35182762145996094},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2969836890697479},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2813602685928345},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2098883092403412},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.06066533923149109},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.04850566387176514},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.54364/aaiml.2025.52216","is_oa":true,"landing_page_url":"https://doi.org/10.54364/aaiml.2025.52216","pdf_url":"https://doi.org/10.54364/aaiml.2025.52216","source":{"id":"https://openalex.org/S4210238872","display_name":"Advances in Artificial Intelligence and Machine Learning","issn_l":"2582-9793","issn":["2582-9793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Artificial Intelligence and Machine Learning","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.54364/aaiml.2025.52216","is_oa":true,"landing_page_url":"https://doi.org/10.54364/aaiml.2025.52216","pdf_url":"https://doi.org/10.54364/aaiml.2025.52216","source":{"id":"https://openalex.org/S4210238872","display_name":"Advances in Artificial Intelligence and Machine Learning","issn_l":"2582-9793","issn":["2582-9793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Artificial Intelligence and Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411887252.pdf","grobid_xml":"https://content.openalex.org/works/W4411887252.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W2025720061","https://openalex.org/W3042623101","https://openalex.org/W3188429639","https://openalex.org/W4385775067","https://openalex.org/W4387706023","https://openalex.org/W4388656597","https://openalex.org/W4390041616","https://openalex.org/W4390740681","https://openalex.org/W4391272424","https://openalex.org/W4399261014","https://openalex.org/W4408069325","https://openalex.org/W6600822972","https://openalex.org/W6609741587","https://openalex.org/W6642591857","https://openalex.org/W6868101485"],"related_works":["https://openalex.org/W4322731367","https://openalex.org/W4390225985","https://openalex.org/W4255298156","https://openalex.org/W2390086507","https://openalex.org/W2387460033","https://openalex.org/W2017376874","https://openalex.org/W2326580408","https://openalex.org/W1638676447","https://openalex.org/W2382672093","https://openalex.org/W2137504275"],"abstract_inverted_index":{"Accurately":[0],"predicting":[1,27],"stock":[2,28,118,153,159],"prices":[3,29],"is":[4],"crucial":[5],"for":[6,26,137,152,156],"investors":[7],"and":[8,42,58,70,102,109,122,163],"policymakers.":[9],"This":[10,143],"paper":[11],"presents":[12],"the":[13,31,138],"first":[14],"empirical":[15],"evaluation":[16],"of":[17,46,90,96],"Lag-Llama,":[18],"a":[19,84,93,149],"novel":[20],"probabilistic":[21],"time":[22],"series":[23,44],"forecasting":[24,115],"model,":[25],"on":[30,92],"Indonesian":[32],"Stock":[33],"Exchange":[34],"(IDX).":[35],"By":[36],"applying":[37],"Lag-Llama":[38,82],"to":[39,54,64,166],"both":[40],"univariate":[41],"multi-time":[43],"forecasts":[45],"key":[47],"IDX":[48],"stocks,":[49],"we":[50],"assess":[51],"its":[52],"ability":[53],"capture":[55],"temporal":[56],"patterns":[57],"market":[59,126],"volatility,":[60],"particularly":[61],"in":[62,79],"comparison":[63],"state-of-the-art":[65],"models":[66],"like":[67],"DeepAR":[68,111],"(RNN)":[69],"Temporal":[71],"Fusion":[72],"Transformer":[73],"(TFT).":[74],"Our":[75],"results":[76],"show":[77],"that":[78],"fine-tuning":[80],"scenarios":[81],"achieves":[83],"Continuous":[85],"Ranked":[86],"Probability":[87],"Score":[88],"(CRPS)":[89],"0.0195":[91],"combined":[94],"dataset":[95],"three":[97],"major":[98],"stocks":[99],"(BBCA,":[100],"BMRI,":[101],"AMRT),":[103],"closely":[104],"matching":[105],"TFT":[106],"(CRPS":[107,112],"0.0179)":[108],"outperforming":[110],"0.0270).":[113],"However,":[114],"across":[116],"broader":[117],"groups":[119],"(Top":[120],"1\u20139":[121],"Top":[123,139],"10\u201318":[124],"by":[125],"capitalization)":[127],"proves":[128],"more":[129],"challenging,":[130],"with":[131],"CRPS":[132],"values":[133],"rising":[134],"(e.g.":[135],"0.0517":[136],"1\u2013":[140],"9":[141],"stocks).":[142],"study":[144],"demonstrates":[145],"Lag-Llama\u2019s":[146],"potential":[147],"as":[148],"robust":[150],"tool":[151],"price":[154],"prediction\u2014particularly":[155],"select,":[157],"closely-related":[158],"groupings\u2014offering":[160],"improved":[161],"precision":[162],"reliability":[164],"compared":[165],"traditional":[167],"methods.":[168]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}