{"id":"https://openalex.org/W4415298697","doi":"https://doi.org/10.1155/acis/6167862","title":"Ethereum Price Prediction Using Time Series and Deep Learning Techniques","display_name":"Ethereum Price Prediction Using Time Series and Deep Learning Techniques","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415298697","doi":"https://doi.org/10.1155/acis/6167862"},"language":"en","primary_location":{"id":"doi:10.1155/acis/6167862","is_oa":true,"landing_page_url":"https://doi.org/10.1155/acis/6167862","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/acis/6167862","source":{"id":"https://openalex.org/S30680879","display_name":"Applied Computational Intelligence and Soft Computing","issn_l":"1687-9724","issn":["1687-9724","1687-9732"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Computational Intelligence and Soft Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/acis/6167862","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068290927","display_name":"Ch. V. Raghavendran","orcid":"https://orcid.org/0000-0002-4423-9086"},"institutions":[{"id":"https://openalex.org/I4210145126","display_name":"Aditya Birla (India)","ror":"https://ror.org/03pztks36","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210145126"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ch. V. Raghavendran","raw_affiliation_strings":["Department of IT ,  Aditya College of Engineering and Technology ,  Surampalem , Andhra Pradesh,  India ,  acet.ac.in"],"raw_orcid":"https://orcid.org/0000-0002-4423-9086","affiliations":[{"raw_affiliation_string":"Department of IT ,  Aditya College of Engineering and Technology ,  Surampalem , Andhra Pradesh,  India ,  acet.ac.in","institution_ids":["https://openalex.org/I4210145126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101817832","display_name":"K. Chandra Mouli","orcid":"https://orcid.org/0009-0001-0889-9263"},"institutions":[{"id":"https://openalex.org/I3131387778","display_name":"Institute of Public Enterprise","ror":"https://ror.org/006sha924","country_code":"IN","type":"education","lineage":["https://openalex.org/I3131387778"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K. Chandra Mouli","raw_affiliation_strings":["Department of CSE ,  Gokaraju Rangaraju Institute of Engineering & Technology ,  Hyderabad , Telangana,  India ,  griet.ac.in"],"raw_orcid":"https://orcid.org/0009-0001-0889-9263","affiliations":[{"raw_affiliation_string":"Department of CSE ,  Gokaraju Rangaraju Institute of Engineering & Technology ,  Hyderabad , Telangana,  India ,  griet.ac.in","institution_ids":["https://openalex.org/I3131387778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106161228","display_name":"Manu Hajari","orcid":"https://orcid.org/0000-0003-3779-2745"},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manu Hajari","raw_affiliation_strings":["Department of CSDS ,  Gokaraju Rangaraju Institute of Engineering & Technology ,  Hyderabad , Telangana,  India ,  griet.ac.in"],"raw_orcid":"https://orcid.org/0000-0003-3779-2745","affiliations":[{"raw_affiliation_string":"Department of CSDS ,  Gokaraju Rangaraju Institute of Engineering & Technology ,  Hyderabad , Telangana,  India ,  griet.ac.in","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012501100","display_name":"A. Anil Kumar Reddy","orcid":null},"institutions":[{"id":"https://openalex.org/I10874241","display_name":"Jawaharlal Nehru Technological University, Hyderabad","ror":"https://ror.org/002tchr49","country_code":"IN","type":"education","lineage":["https://openalex.org/I10874241"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"A. Anil Kumar Reddy","raw_affiliation_strings":["Department of CSE & CSM ,  Samskruti College of Engineering and Technology ,  Hyderabad ,  501301 , Telangana,  India"],"raw_orcid":"https://orcid.org/0009-0009-8910-161X","affiliations":[{"raw_affiliation_string":"Department of CSE & CSM ,  Samskruti College of Engineering and Technology ,  Hyderabad ,  501301 , Telangana,  India","institution_ids":["https://openalex.org/I10874241"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047860563","display_name":"V. Shiva Narayana Reddy","orcid":"https://orcid.org/0000-0002-1837-3513"},"institutions":[{"id":"https://openalex.org/I10874241","display_name":"Jawaharlal Nehru Technological University, Hyderabad","ror":"https://ror.org/002tchr49","country_code":"IN","type":"education","lineage":["https://openalex.org/I10874241"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"V. Shiva Narayana Reddy","raw_affiliation_strings":["Department of CSE & CSM ,  Samskruti College of Engineering and Technology ,  Hyderabad ,  501301 , Telangana,  India"],"raw_orcid":"https://orcid.org/0000-0002-1837-3513","affiliations":[{"raw_affiliation_string":"Department of CSE & CSM ,  Samskruti College of Engineering and Technology ,  Hyderabad ,  501301 , Telangana,  India","institution_ids":["https://openalex.org/I10874241"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081713064","display_name":"Perumalla Janaki Ramulu","orcid":"https://orcid.org/0000-0002-7856-8638"},"institutions":[{"id":"https://openalex.org/I176150703","display_name":"University of the Gambia","ror":"https://ror.org/038tkkk06","country_code":"GM","type":"education","lineage":["https://openalex.org/I176150703"]}],"countries":["GM"],"is_corresponding":true,"raw_author_name":"Janaki Ramulu Perumalla","raw_affiliation_strings":["Department of Mechanical Engineering ,  Gambella University ,  Gambela ,  Ethiopia"],"raw_orcid":"https://orcid.org/0000-0002-7856-8638","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering ,  Gambella University ,  Gambela ,  Ethiopia","institution_ids":["https://openalex.org/I176150703"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5081713064"],"corresponding_institution_ids":["https://openalex.org/I176150703"],"apc_list":{"value":900,"currency":"USD","value_usd":900},"apc_paid":{"value":900,"currency":"USD","value_usd":900},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31770628,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2025","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":0.9973000288009644,"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":0.9973000288009644,"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/T14319","display_name":"Currency Recognition and Detection","score":0.9779999852180481,"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"}},{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9732999801635742,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.900600016117096},{"id":"https://openalex.org/keywords/cryptocurrency","display_name":"Cryptocurrency","score":0.8051999807357788},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6105999946594238},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5270000100135803},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.48919999599456787},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.4724999964237213},{"id":"https://openalex.org/keywords/valuation","display_name":"Valuation (finance)","score":0.45820000767707825},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.40849998593330383},{"id":"https://openalex.org/keywords/asset","display_name":"Asset (computer security)","score":0.38830000162124634}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.900600016117096},{"id":"https://openalex.org/C180706569","wikidata":"https://www.wikidata.org/wiki/Q13479982","display_name":"Cryptocurrency","level":2,"score":0.8051999807357788},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7752000093460083},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6105999946594238},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5727999806404114},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5270000100135803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48919999599456787},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.48919999599456787},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.4724999964237213},{"id":"https://openalex.org/C186027771","wikidata":"https://www.wikidata.org/wiki/Q4008379","display_name":"Valuation (finance)","level":2,"score":0.45820000767707825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4325000047683716},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C76178495","wikidata":"https://www.wikidata.org/wiki/Q4808784","display_name":"Asset (computer security)","level":2,"score":0.38830000162124634},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.3873000144958496},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3779999911785126},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.361299991607666},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2818000018596649},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.25690001249313354},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1155/acis/6167862","is_oa":true,"landing_page_url":"https://doi.org/10.1155/acis/6167862","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/acis/6167862","source":{"id":"https://openalex.org/S30680879","display_name":"Applied Computational Intelligence and Soft Computing","issn_l":"1687-9724","issn":["1687-9724","1687-9732"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Computational Intelligence and Soft Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b552aeba6fc148d6b3c3e72b514509e1","is_oa":true,"landing_page_url":"https://doaj.org/article/b552aeba6fc148d6b3c3e72b514509e1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Computational Intelligence and Soft Computing, Vol 2025 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/acis/6167862","is_oa":true,"landing_page_url":"https://doi.org/10.1155/acis/6167862","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/acis/6167862","source":{"id":"https://openalex.org/S30680879","display_name":"Applied Computational Intelligence and Soft Computing","issn_l":"1687-9724","issn":["1687-9724","1687-9732"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Computational Intelligence and Soft Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415298697.pdf","grobid_xml":"https://content.openalex.org/works/W4415298697.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W2042442456","https://openalex.org/W2293744262","https://openalex.org/W2586112617","https://openalex.org/W2747599906","https://openalex.org/W2901487042","https://openalex.org/W2910062285","https://openalex.org/W2965263876","https://openalex.org/W2967732991","https://openalex.org/W3019959235","https://openalex.org/W4249224151","https://openalex.org/W4292081177","https://openalex.org/W4292483811","https://openalex.org/W4316498370","https://openalex.org/W4318484724","https://openalex.org/W4321374412","https://openalex.org/W4327981843","https://openalex.org/W4387059113","https://openalex.org/W4395955899","https://openalex.org/W4399274202","https://openalex.org/W4401408109","https://openalex.org/W4401565021"],"related_works":[],"abstract_inverted_index":{"Predictive":[0],"modeling":[1,193],"has":[2],"emerged":[3],"as":[4,72,74,88],"a":[5,89],"key":[6],"focus":[7],"for":[8,141,207,217],"cryptocurrency":[9,99,208],"market":[10,20,219],"asset":[11,240],"valuation":[12],"due":[13],"to":[14,112,153,170,228],"its":[15],"complex":[16],"nature":[17],"and":[18,38,42,55,63,77,130,133,165,173,184,230],"high":[19],"volatility.":[21],"The":[22,66,83,124,179],"research":[23,198,223],"looks":[24],"into":[25],"Ethereum":[26,53,80,148,176,194],"price":[27,81,100,149,177,195,209],"forecasting":[28,90,104,205],"with":[29,105,117,146,168,188],"the":[30,155,200,237],"methods":[31,206],"of":[32,127,139,202],"autoregressive":[33],"integrated":[34],"moving":[35],"average":[36],"(ARIMA)":[37],"Facebook":[39,84,185],"Prophet":[40,85,186],"model":[41,68,86],"long":[43],"short\u2010term":[44],"memory":[45],"(LSTM)":[46],"networks.":[47],"These":[48],"models":[49,132,156],"operate":[50],"on":[51],"historical":[52],"prices":[54],"show":[56],"their":[57],"efficiency":[58],"regarding":[59],"temporal":[60],"pattern":[61],"recognition":[62],"prediction":[64,210],"accuracy.":[65,147],"ARIMA":[67,183],"helps":[69],"reveal":[70,171],"trends":[71],"well":[73],"seasonal":[75],"patterns":[76,115],"irregularities":[78],"within":[79,98,236],"fluctuations.":[82,196],"serves":[87],"tool":[91],"because":[92],"it":[93],"automatically":[94],"handles":[95],"peculiarities":[96],"present":[97],"data.":[101],"Time":[102],"series":[103,144,204],"LSTMs":[106,140],"becomes":[107],"an":[108],"advanced":[109],"technique":[110],"used":[111],"detect":[113],"intricate":[114],"along":[116,167],"sustained":[118],"dependency":[119],"relationships":[120],"between":[121],"data":[122,129,145],"points.":[123],"systematic":[125],"process":[126],"preparing":[128],"constructing":[131],"assessing":[134],"results":[135],"enables":[136],"proper":[137],"utilization":[138],"predicting":[142],"time":[143,203],"datasets":[150],"are":[151],"applied":[152],"train":[154],"which":[157],"undergo":[158],"performance":[159],"evaluation":[160,180],"using":[161],"MPE":[162],"alongside":[163],"MAPE":[164],"RMSE":[166],"MAE":[169],"strengths":[172],"weaknesses":[174],"during":[175],"predictions.":[178],"shows":[181],"that":[182],"together":[187],"LSTM":[189],"demonstrate":[190],"success":[191],"in":[192],"This":[197],"explores":[199],"effectiveness":[201],"yielding":[211],"vital":[212],"knowledge":[213,227],"about":[214],"reliable":[215],"tools":[216],"financial":[218],"trend":[220],"modeling.":[221],"Current":[222],"findings":[224],"will":[225],"provide":[226],"investors":[229],"risk":[231],"management":[232],"professionals":[233],"making":[234],"decisions":[235],"volatile":[238],"digital":[239],"space.":[241]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-18T00:00:00"}
