{"id":"https://openalex.org/W4392982161","doi":"https://doi.org/10.1109/cvmi59935.2023.10465177","title":"Improving Stock Market Forecasting Using Advanced Time Series Analysis Techniques","display_name":"Improving Stock Market Forecasting Using Advanced Time Series Analysis Techniques","publication_year":2023,"publication_date":"2023-12-10","ids":{"openalex":"https://openalex.org/W4392982161","doi":"https://doi.org/10.1109/cvmi59935.2023.10465177"},"language":"en","primary_location":{"id":"doi:10.1109/cvmi59935.2023.10465177","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cvmi59935.2023.10465177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","raw_type":"proceedings-article"},"type":"article","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/A5113780321","display_name":"Shivamshu Roy","orcid":null},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Shivamshu Roy","raw_affiliation_strings":["SCSET Bennett University,Greater Noida,UP,India","SCSET Bennett University, Greater Noida, UP, India"],"affiliations":[{"raw_affiliation_string":"SCSET Bennett University,Greater Noida,UP,India","institution_ids":["https://openalex.org/I3129773123"]},{"raw_affiliation_string":"SCSET Bennett University, Greater Noida, UP, India","institution_ids":["https://openalex.org/I3129773123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073411679","display_name":"Vimal Kumar","orcid":"https://orcid.org/0000-0003-1038-1102"},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vimal Kumar","raw_affiliation_strings":["SCSET Bennett University,Greater Noida,UP,India","SCSET Bennett University, Greater Noida, UP, India"],"affiliations":[{"raw_affiliation_string":"SCSET Bennett University,Greater Noida,UP,India","institution_ids":["https://openalex.org/I3129773123"]},{"raw_affiliation_string":"SCSET Bennett University, Greater Noida, UP, India","institution_ids":["https://openalex.org/I3129773123"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065833967","display_name":"Umesh Gupta","orcid":"https://orcid.org/0000-0001-8947-2749"},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Umesh Gupta","raw_affiliation_strings":["SCSET Bennett University,Greater Noida,UP,India","SCSET Bennett University, Greater Noida, UP, India"],"affiliations":[{"raw_affiliation_string":"SCSET Bennett University,Greater Noida,UP,India","institution_ids":["https://openalex.org/I3129773123"]},{"raw_affiliation_string":"SCSET Bennett University, Greater Noida, UP, India","institution_ids":["https://openalex.org/I3129773123"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5113780321"],"corresponding_institution_ids":["https://openalex.org/I3129773123"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26923639,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.8844000101089478,"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":0.8844000101089478,"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":0.8051999807357788,"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/time-series","display_name":"Time series","score":0.6213641166687012},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5916045904159546},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5739141702651978},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5069339871406555},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.424093097448349},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19278278946876526},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13738229870796204}],"concepts":[{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6213641166687012},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5916045904159546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5739141702651978},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5069339871406555},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.424093097448349},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19278278946876526},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13738229870796204},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"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/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvmi59935.2023.10465177","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cvmi59935.2023.10465177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2081737938","https://openalex.org/W2165297877","https://openalex.org/W2171168084","https://openalex.org/W2603499310","https://openalex.org/W2970016095","https://openalex.org/W2971856398","https://openalex.org/W2984376566","https://openalex.org/W3009650506","https://openalex.org/W3017116930","https://openalex.org/W3122551555","https://openalex.org/W4241115065","https://openalex.org/W6674457323"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660","https://openalex.org/W2370669686"],"abstract_inverted_index":{"The":[0],"mechanism":[1],"of":[2,120,126,151,171],"a":[3,66,134,168,184],"stock":[4,24,40,131],"exchange":[5],"is":[6,31,43],"very":[7],"dynamic":[8],"and":[9,27,48,62,85,104,109,113,123,155,164],"intricate,":[10],"making":[11],"it":[12],"difficult":[13,135],"to":[14,50,82,95,106,111,147],"forecast":[15],"its":[16],"conduct.":[17],"A":[18],"typical":[19],"method":[20],"for":[21,45],"analyzing":[22],"historical":[23],"market":[25,41,53,132],"data":[26,34,42],"predicting":[28],"future":[29],"trends":[30],"time":[32,140],"series":[33,141],"analysis.":[35],"With":[36],"this":[37],"strategy,":[38],"past":[39,100],"examined":[44],"patterns,":[46],"trends,":[47],"correlations":[49],"spot":[51],"prospective":[52],"shifts":[54],"in":[55,71,88,139],"the":[56,121,127,130,149,152],"future.":[57],"Moving":[58],"averages,":[59],"autoregressive":[60],"models,":[61],"exponential":[63],"smoothing":[64],"are":[65],"few":[67],"statistical":[68],"methods":[69,163],"used":[70,94],"Improving":[72,172],"Stock":[73,173],"Market":[74,174],"Forecasting":[75,175],"using":[76,160,176],"Advanced":[77,177],"Time":[78,178],"Series":[79,179],"Analysis":[80,180],"Techniques":[81],"find":[83],"periodic":[84],"unusual":[86],"changes":[87],"data.":[89],"These":[90],"techniques":[91],"may":[92],"be":[93],"provide":[96],"projections":[97],"based":[98],"on":[99],"data,":[101],"empowering":[102],"traders,":[103],"investors":[105],"choose":[107],"when":[108],"how":[110],"purchase":[112],"sell":[114],"stocks":[115],"with":[116],"more":[117,185],"knowledge.":[118],"Because":[119],"erratic":[122],"unpredictable":[124],"nature":[125],"market,":[128],"forecasting":[129,162],"remains":[133],"subject":[136],"despite":[137],"advances":[138],"analytic":[142],"tools.":[143],"This":[144],"research":[145],"intends":[146],"improve":[148],"precision":[150],"Holt-Winters":[153],"model":[154],"Simple":[156],"moving":[157],"average":[158],"by":[159],"so-phisticated":[161],"optimisation":[165],"algorithms":[166],"via":[167],"thorough":[169],"investigation":[170],"prediction":[181],"which":[182],"gives":[183],"efficient":[186],"result":[187]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}