{"id":"https://openalex.org/W4396879076","doi":"https://doi.org/10.1145/3647444.3652440","title":"An Empirical Study on: Time Series Forecasting of Amazon Stock Prices using Neural Networks LSTM and GAN models","display_name":"An Empirical Study on: Time Series Forecasting of Amazon Stock Prices using Neural Networks LSTM and GAN models","publication_year":2023,"publication_date":"2023-11-23","ids":{"openalex":"https://openalex.org/W4396879076","doi":"https://doi.org/10.1145/3647444.3652440"},"language":"en","primary_location":{"id":"doi:10.1145/3647444.3652440","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3647444.3652440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Information Management & Machine Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","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/A5033816650","display_name":"Anjul Bhardwaj","orcid":"https://orcid.org/0000-0001-8305-1361"},"institutions":[{"id":"https://openalex.org/I4210126659","display_name":"Poornima University","ror":"https://ror.org/03gnqp653","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210126659"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anjul Bhardwaj","raw_affiliation_strings":["Artificial Intelligence & Data Science, Poornima Institute of Engineering & Technology, India"],"raw_orcid":"https://orcid.org/0000-0001-8305-1361","affiliations":[{"raw_affiliation_string":"Artificial Intelligence & Data Science, Poornima Institute of Engineering & Technology, India","institution_ids":["https://openalex.org/I4210126659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060419096","display_name":"Uday Pratap Singh","orcid":"https://orcid.org/0000-0001-7573-8550"},"institutions":[{"id":"https://openalex.org/I4210126659","display_name":"Poornima University","ror":"https://ror.org/03gnqp653","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210126659"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Uday Pratap Singh","raw_affiliation_strings":["Department of AI & DS, Poornima Institute of Engineering and Technology, India"],"raw_orcid":"https://orcid.org/0000-0001-7573-8550","affiliations":[{"raw_affiliation_string":"Department of AI & DS, Poornima Institute of Engineering and Technology, India","institution_ids":["https://openalex.org/I4210126659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210126659"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9965999722480774,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.987500011920929,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.676159143447876},{"id":"https://openalex.org/keywords/amazon-rainforest","display_name":"Amazon rainforest","score":0.6481865048408508},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6301562786102295},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5455167293548584},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5404049158096313},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5231876969337463},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.475887656211853},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.466988742351532},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.43724358081817627},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32499608397483826},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.1782856583595276},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10762014985084534},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09610810875892639}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.676159143447876},{"id":"https://openalex.org/C535291247","wikidata":"https://www.wikidata.org/wiki/Q177567","display_name":"Amazon rainforest","level":2,"score":0.6481865048408508},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6301562786102295},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5455167293548584},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5404049158096313},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5231876969337463},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.475887656211853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.466988742351532},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.43724358081817627},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32499608397483826},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.1782856583595276},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10762014985084534},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09610810875892639},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","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},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3647444.3652440","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3647444.3652440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Information Management & Machine Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2070773792","https://openalex.org/W2084072817","https://openalex.org/W2085444045","https://openalex.org/W2233997611","https://openalex.org/W2911608625","https://openalex.org/W2945346514","https://openalex.org/W2955471158","https://openalex.org/W2980136338","https://openalex.org/W3016744576","https://openalex.org/W3080113688","https://openalex.org/W3156510899","https://openalex.org/W3202169040","https://openalex.org/W4200094460","https://openalex.org/W4210356397","https://openalex.org/W4226160099","https://openalex.org/W4293170968","https://openalex.org/W4307216807","https://openalex.org/W4307941912","https://openalex.org/W4308417351","https://openalex.org/W4308748513","https://openalex.org/W4311002964","https://openalex.org/W4379932692","https://openalex.org/W4379932916","https://openalex.org/W4379932966"],"related_works":["https://openalex.org/W3022229171","https://openalex.org/W2913190967","https://openalex.org/W587719479","https://openalex.org/W3165307885","https://openalex.org/W3097390808","https://openalex.org/W2611724343","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660","https://openalex.org/W2370669686"],"abstract_inverted_index":{"The":[0,187],"OHLCV":[1,103],"(Open,":[2],"High,":[3],"Low,":[4],"Close,":[5],"Volume)":[6],"data":[7,84,104,132,181],"used":[8,13],"in":[9,62,182],"this":[10,66],"study":[11],"is":[12,58],"to":[14,59,70,123,133,145,216],"forecast":[15,217],"time":[16,184],"series":[17,185],"and":[18,34,45,89,106,110,113,153,178,192,213],"anticipate":[19],"stock":[20,53,63,83,218],"price":[21,219],"movement.":[22],"We":[23,75,98,120],"investigate":[24],"a":[25,94,160,173,183],"wide":[26],"variety":[27],"of":[28,49,168,189,209],"models,":[29,170],"including":[30],"traditional":[31],"statistical":[32],"approaches":[33],"cutting-edge":[35],"deep":[36,126],"learning":[37,127],"strategies":[38],"combined":[39],"with":[40],"sentiment":[41,143,158],"analysis,":[42],"feature":[43],"extraction,":[44],"hyperparameter":[46],"tweaking.":[47],"Instead":[48],"focusing":[50],"on":[51,93,101,206],"absolute":[52],"prices,":[54,64],"our":[55,77,169,202],"main":[56],"goal":[57],"predict":[60],"swings":[61],"as":[65,159],"has":[67],"been":[68],"shown":[69],"produce":[71],"more":[72,124],"accurate":[73],"outcomes.":[74],"start":[76],"research":[78],"by":[79],"obtaining":[80],"historical":[81],"Amazon":[82],"via":[85],"the":[86,102,130,146,166,180,199,207],"Yahoo":[87],"API,":[88],"then":[90,107,121],"we":[91,108,141,171],"go":[92],"thorough":[95],"analytical":[96],"journey.":[97],"generate":[99],"features":[100],"first,":[105],"design":[109],"test":[111],"Fourier":[112],"Autoregressive":[114],"Integrated":[115],"Moving":[116],"Average":[117],"(ARIMA)":[118],"models.":[119,139],"switch":[122],"sophisticated":[125],"methods,":[128],"using":[129],"pre-processed":[131],"apply":[134],"Long":[135],"Short-Term":[136],"Memory":[137],"(LSTM)":[138],"Interestingly,":[140],"add":[142],"analysis":[144],"LSTM":[147],"study,":[148],"which":[149],"expands":[150],"its":[151],"scope":[152],"lets":[154],"us":[155],"consider":[156],"market":[157],"possible":[161],"influencing":[162],"factor.":[163],"To":[164],"guarantee":[165],"stability":[167],"use":[172],"careful":[174],"train-test":[175],"split":[176],"technique":[177],"organize":[179],"manner.":[186],"field":[188],"financial":[190],"forecasting":[191],"trading":[193],"methods":[194],"will":[195],"ultimately":[196],"benefit":[197],"from":[198],"insightful":[200],"information":[201],"study's":[203],"findings":[204],"provide":[205],"efficacy":[208],"different":[210],"modeling":[211],"techniques":[212],"their":[214],"capacity":[215],"movements.":[220]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}