{"id":"https://openalex.org/W4415420702","doi":"https://doi.org/10.1007/s44163-025-00464-w","title":"Forecasting gold price using hybrid deep neural network LSTM-autoencoder","display_name":"Forecasting gold price using hybrid deep neural network LSTM-autoencoder","publication_year":2025,"publication_date":"2025-10-22","ids":{"openalex":"https://openalex.org/W4415420702","doi":"https://doi.org/10.1007/s44163-025-00464-w"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-025-00464-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00464-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00464-w.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00464-w.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111356848","display_name":"Agampreet Saini","orcid":"https://orcid.org/0009-0007-2633-6580"},"institutions":[{"id":"https://openalex.org/I5847235","display_name":"University of Petroleum and Energy Studies","ror":"https://ror.org/04q2jes40","country_code":"IN","type":"education","lineage":["https://openalex.org/I5847235"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Agampreet Saini","raw_affiliation_strings":["SOCS, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, 248007, India"],"affiliations":[{"raw_affiliation_string":"SOCS, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, 248007, India","institution_ids":["https://openalex.org/I5847235"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038898220","display_name":"Rahul Kumar Singh","orcid":"https://orcid.org/0000-0002-4996-5300"},"institutions":[{"id":"https://openalex.org/I5847235","display_name":"University of Petroleum and Energy Studies","ror":"https://ror.org/04q2jes40","country_code":"IN","type":"education","lineage":["https://openalex.org/I5847235"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rahul Kumar Singh","raw_affiliation_strings":["SOCS, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, 248007, India"],"affiliations":[{"raw_affiliation_string":"SOCS, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, 248007, India","institution_ids":["https://openalex.org/I5847235"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114143567","display_name":"Puneet Sinha","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101034","display_name":"Birla Institute of Technology and Science - Hyderabad Campus","ror":"https://ror.org/014ctt859","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210101034","https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Puneet Sinha","raw_affiliation_strings":["BITS Pilani, Hyderabad Campus, Secunderabad, India"],"affiliations":[{"raw_affiliation_string":"BITS Pilani, Hyderabad Campus, Secunderabad, India","institution_ids":["https://openalex.org/I4210101034"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5111356848"],"corresponding_institution_ids":["https://openalex.org/I5847235"],"apc_list":{"value":990,"currency":"EUR","value_usd":1067},"apc_paid":{"value":990,"currency":"EUR","value_usd":1067},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36040577,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9976999759674072,"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":0.9976999759674072,"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/T11059","display_name":"Market Dynamics and Volatility","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"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/T14319","display_name":"Currency Recognition and Detection","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/overfitting","display_name":"Overfitting","score":0.6579999923706055},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6205000281333923},{"id":"https://openalex.org/keywords/gold-as-an-investment","display_name":"Gold as an investment","score":0.5722000002861023},{"id":"https://openalex.org/keywords/us-dollar","display_name":"Us dollar","score":0.5023999810218811},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48240000009536743},{"id":"https://openalex.org/keywords/liberian-dollar","display_name":"Liberian dollar","score":0.4812999963760376},{"id":"https://openalex.org/keywords/currency","display_name":"Currency","score":0.45730000734329224},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.40950000286102295},{"id":"https://openalex.org/keywords/west-texas-intermediate","display_name":"West Texas Intermediate","score":0.40119999647140503}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6579999923706055},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6205000281333923},{"id":"https://openalex.org/C11039479","wikidata":"https://www.wikidata.org/wiki/Q791975","display_name":"Gold as an investment","level":2,"score":0.5722000002861023},{"id":"https://openalex.org/C3019931467","wikidata":"https://www.wikidata.org/wiki/Q4917","display_name":"Us dollar","level":3,"score":0.5023999810218811},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.49889999628067017},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48240000009536743},{"id":"https://openalex.org/C109168655","wikidata":"https://www.wikidata.org/wiki/Q242988","display_name":"Liberian dollar","level":2,"score":0.4812999963760376},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.4812999963760376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47600001096725464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4636000096797943},{"id":"https://openalex.org/C141121606","wikidata":"https://www.wikidata.org/wiki/Q8142","display_name":"Currency","level":2,"score":0.45730000734329224},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.40950000286102295},{"id":"https://openalex.org/C70784835","wikidata":"https://www.wikidata.org/wiki/Q1049071","display_name":"West Texas Intermediate","level":3,"score":0.40119999647140503},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.38019999861717224},{"id":"https://openalex.org/C2987168347","wikidata":"https://www.wikidata.org/wiki/Q22656","display_name":"Crude oil","level":2,"score":0.38019999861717224},{"id":"https://openalex.org/C2778146478","wikidata":"https://www.wikidata.org/wiki/Q271449","display_name":"Precious metal","level":3,"score":0.37549999356269836},{"id":"https://openalex.org/C2984556133","wikidata":"https://www.wikidata.org/wiki/Q297279","display_name":"Oil price","level":2,"score":0.3752000033855438},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36169999837875366},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C163068380","wikidata":"https://www.wikidata.org/wiki/Q3409313","display_name":"Economic forecasting","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C2779544567","wikidata":"https://www.wikidata.org/wiki/Q7621877","display_name":"Strategist","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C2776988154","wikidata":"https://www.wikidata.org/wiki/Q66100","display_name":"Exchange rate","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C2779990667","wikidata":"https://www.wikidata.org/wiki/Q5953266","display_name":"Hybrid neural network","level":3,"score":0.28769999742507935},{"id":"https://openalex.org/C105289051","wikidata":"https://www.wikidata.org/wiki/Q1930094","display_name":"Pillar","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C106306483","wikidata":"https://www.wikidata.org/wiki/Q183984","display_name":"Futures contract","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C138253771","wikidata":"https://www.wikidata.org/wiki/Q536652","display_name":"U.S. Dollar Index","level":4,"score":0.2646999955177307},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.25049999356269836}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44163-025-00464-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00464-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00464-w.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8664dff01c3d468cb12b3b530daf8eb2","is_oa":true,"landing_page_url":"https://doaj.org/article/8664dff01c3d468cb12b3b530daf8eb2","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Discover Artificial Intelligence, Vol 5, Iss 1, Pp 1-19 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44163-025-00464-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00464-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00464-w.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4415420702.pdf"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W2487770199","https://openalex.org/W2502759836","https://openalex.org/W2896556344","https://openalex.org/W2999967345","https://openalex.org/W3021785726","https://openalex.org/W3027412388","https://openalex.org/W4230452299","https://openalex.org/W4392193587","https://openalex.org/W4392564092","https://openalex.org/W4394005832","https://openalex.org/W4402416387"],"related_works":[],"abstract_inverted_index":{"Gold":[0],"prices":[1,86,132,148],"hold":[2],"a":[3,121,134,160],"significant":[4],"place":[5],"in":[6,49,194,201],"the":[7,13,84,88,100,138,146,153,166,172,176,181,196],"global":[8],"economy":[9],"as":[10,106,133],"they":[11],"reflect":[12],"economic":[14,216],"health,":[15],"which":[16,66,208],"influences":[17],"markets,":[18],"investments,":[19],"and":[20,29,47,112,188,215],"currency":[21],"values.":[22],"Industries":[23],"that":[24],"rely":[25],"on":[26,116],"commodities,":[27],"investors":[28],"decision-makers,":[30],"need":[31],"accurate":[32],"forecasting":[33,117],"gold":[34,39,85,118,131,154],"prices.":[35,119],"Existing":[36],"models":[37,63],"for":[38,87],"price":[40,115,155],"forecasting,":[41],"struggle":[42],"with":[43,152],"overfitting,":[44],"poor":[45],"adaptability":[46],"difficulty":[48],"handling":[50],"volatile":[51],"or":[52],"long":[53],"term":[54],"trend":[55],"changes.":[56],"For":[57],"this":[58,191],"research,":[59],"various":[60,142],"deep":[61,199],"learning":[62,200],"were":[64],"evaluated,":[65],"includes":[67],"Long":[68],"Short-Term":[69],"Memory,":[70],"Convolutional":[71],"Neural":[72],"Networks,":[73],"hybrid":[74,78,168],"LSTM-CNN.":[75],"The":[76,186],"proposed":[77],"model":[79,169],"of":[80,102,124,183,198],"LSTM-Autoencoders,":[81],"to":[82,94,210],"predict":[83],"data":[89],"taken":[90],"from":[91,190],"September":[92],"2000":[93],"January":[95],"2024.":[96],"We":[97],"also":[98],"examine":[99],"impact":[101],"external":[103,126],"parameters":[104,127],"such":[105],"US":[107],"dollar":[108],"price,":[109,111],"silver":[110,147],"crude":[113,157],"oil":[114,158],"Furthermore,":[120],"comparative":[122],"analysis":[123],"these":[125],"shows":[128],"that,":[129],"only":[130],"prediction":[135],"parameter":[136],"yields":[137],"highest":[139,173],"accuracy":[140,174],"across":[141],"evaluation":[143],"metrics.":[144],"While":[145],"showed":[149],"some":[150],"association":[151],"prediction,":[156],"had":[159],"comparatively":[161],"low":[162],"predictive":[163],"value.":[164],"Additionally":[165],"LSTM-Autoencoders":[167],"has":[170],"shown":[171],"outperforming":[175],"other":[177],"models,":[178],"while":[179],"addressing":[180],"challenge":[182],"overfitting":[184],"effectively.":[185],"results":[187],"findings":[189],"study,":[192],"aid":[193],"exploring":[195],"role":[197],"financial":[202,211],"time":[203],"series":[204],"domain,":[205],"offering":[206],"insights":[207],"contributes":[209],"analysts":[212],"market":[213],"strategist":[214],"forecasters.":[217]},"counts_by_year":[],"updated_date":"2026-03-17T17:19:04.345684","created_date":"2025-10-24T00:00:00"}