{"id":"https://openalex.org/W2013255028","doi":"https://doi.org/10.1109/icbim.2014.6970973","title":"Forecasting of Indian stock market using time-series ARIMA model","display_name":"Forecasting of Indian stock market using time-series ARIMA model","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2013255028","doi":"https://doi.org/10.1109/icbim.2014.6970973","mag":"2013255028"},"language":"en","primary_location":{"id":"doi:10.1109/icbim.2014.6970973","doi":"https://doi.org/10.1109/icbim.2014.6970973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbim.2014.6970973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 2nd International Conference on Business and Information Management (ICBIM)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5086721044","display_name":"Debadrita Banerjee","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405256019","display_name":"St. Xavier's College (Autonomous), Kolkata","ror":"https://ror.org/03p0rew30","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405256019"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Debadrita Banerjee","raw_affiliation_strings":["Student Department of Statistics, St. Xavier's College, Kolkata, India","Department of Statistics St. Xavier's College Kolkata India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Student Department of Statistics, St. Xavier's College, Kolkata, India","institution_ids":["https://openalex.org/I4405256019"]},{"raw_affiliation_string":"Department of Statistics St. Xavier's College Kolkata India","institution_ids":["https://openalex.org/I4405256019"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5086721044"],"corresponding_institution_ids":["https://openalex.org/I4405256019"],"apc_list":null,"apc_paid":null,"fwci":2.986,"has_fulltext":false,"cited_by_count":90,"citation_normalized_percentile":{"value":0.90085911,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"131","last_page":"135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9980000257492065,"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":0.9980000257492065,"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":0.0019000000320374966,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/stock-market-forecasting","display_name":"stock market prediction","score":0.9010000228881836},{"id":"https://openalex.org/keywords/indian-stock-market","display_name":"Indian stock market","score":0.9139999747276306},{"id":"https://openalex.org/keywords/bse-sensex","display_name":"BSE Sensex","score":0.8149999976158142},{"id":"https://openalex.org/keywords/arima-model","display_name":"ARIMA model","score":0.9259999990463257},{"id":"https://openalex.org/keywords/stock-index-forecasting","display_name":"stock index forecasting","score":0.7200000286102295},{"id":"https://openalex.org/keywords/investment-decisions","display_name":"investment decision-making","score":0.5070000290870667}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.8362438678741455},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.7162242531776428},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6218345165252686},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5809792280197144},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.5634359121322632},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5494824647903442},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4764554798603058},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3822175860404968},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.3748742938041687},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3646619915962219},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3590046167373657},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.174716979265213},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14214184880256653},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10803943872451782},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icbim.2014.6970973","doi":"https://doi.org/10.1109/icbim.2014.6970973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbim.2014.6970973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 2nd International Conference on Business and Information Management (ICBIM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1521453743","https://openalex.org/W1963511811","https://openalex.org/W2073332965","https://openalex.org/W2126455177","https://openalex.org/W2126831543","https://openalex.org/W3125339891","https://openalex.org/W3150435615","https://openalex.org/W4205502210","https://openalex.org/W4292483811","https://openalex.org/W6701217941"],"related_works":["https://openalex.org/W6885104135","https://openalex.org/W4313116698","https://openalex.org/W6968143732","https://openalex.org/W4249391648","https://openalex.org/W3141625852","https://openalex.org/W3176439250","https://openalex.org/W2972337921","https://openalex.org/W7134140677","https://openalex.org/W2331820705","https://openalex.org/W7221400095"],"abstract_inverted_index":{"The":[0,125],"most":[1],"reliable":[2],"way":[3],"to":[4,9,11,38,42,75,84,148],"forecast":[5,85],"the":[6,13,25,28,32,58,86,91,109,119,122,130,154,158,162,166,170],"future":[7,46,87,110],"is":[8,129,143],"try":[10,41],"understand":[12,39],"present":[14,29,155],"and":[15,40,68,138,140],"thus,":[16],"accordingly":[17],"we":[18,53,72,107,164],"have":[19,54,73,114,149],"set":[20],"our":[21],"prior":[22],"objective":[23],"as":[24,37],"analysis":[26],"of":[27,31,63,90,101,121,132,153,157,173,175],"scenario":[30],"Indian":[33,92,123,126],"Stock":[34,127],"Market":[35],"so":[36],"create":[43],"a":[44,115,150],"better":[45],"scope":[47],"for":[48,65,134,146],"investment.":[49],"On":[50],"this":[51],"context,":[52],"collected":[55],"data":[56,172],"on":[57,70,105,118],"monthly":[59],"closing":[60],"stock":[61,93,111],"indices":[62,112],"sensex":[64,174],"six":[66],"years(2007-2012)":[67],"based":[69,104],"these":[71],"tried":[74],"develop":[76],"an":[77,99],"appropriate":[78],"model":[79,103,163],"which":[80,106,113],"would":[81],"help":[82],"us":[83],"unobserved":[88],"values":[89],"market":[94,128],"indices.":[95],"This":[96],"study":[97],"offers":[98],"application":[100],"ARIMA":[102],"predict":[108],"strong":[116],"influence":[117],"performance":[120],"economy.":[124],"centre":[131],"interest":[133],"many":[135],"economists,":[136],"investors":[137],"researchers":[139],"hence":[141],"it":[142],"quite":[144],"important":[145],"them":[147],"clear":[151],"understanding":[152],"status":[156],"market.":[159],"To":[160],"establish":[161],"applied":[165],"validation":[167],"technique":[168],"with":[169],"observed":[171],"2013.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2015,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2016-06-24T00:00:00"}