{"id":"https://openalex.org/W2997549107","doi":"https://doi.org/10.1109/icccnt45670.2019.8944624","title":"Financial and Non-Stationary Time Series Forecasting using LSTM Recurrent Neural Network for Short and Long Horizon","display_name":"Financial and Non-Stationary Time Series Forecasting using LSTM Recurrent Neural Network for Short and Long Horizon","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2997549107","doi":"https://doi.org/10.1109/icccnt45670.2019.8944624","mag":"2997549107"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt45670.2019.8944624","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt45670.2019.8944624","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","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/A5100747952","display_name":"Preeti Preeti","orcid":null},"institutions":[{"id":"https://openalex.org/I110166357","display_name":"University of Delhi","ror":"https://ror.org/04gzb2213","country_code":"IN","type":"education","lineage":["https://openalex.org/I110166357"]},{"id":"https://openalex.org/I4210095530","display_name":"Deen Dayal Upadhyay Hospital","ror":"https://ror.org/00n3vh666","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I4210095530"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Preeti","raw_affiliation_strings":["Department of Computer Science, Deen Dayal Upadhyaya College University of Delhi, Delhi, INDIA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Deen Dayal Upadhyaya College University of Delhi, Delhi, INDIA","institution_ids":["https://openalex.org/I4210095530","https://openalex.org/I110166357"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108701146","display_name":"Rajni Bala","orcid":null},"institutions":[{"id":"https://openalex.org/I4210095530","display_name":"Deen Dayal Upadhyay Hospital","ror":"https://ror.org/00n3vh666","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I4210095530"]},{"id":"https://openalex.org/I110166357","display_name":"University of Delhi","ror":"https://ror.org/04gzb2213","country_code":"IN","type":"education","lineage":["https://openalex.org/I110166357"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajni Bala","raw_affiliation_strings":["Department of Computer Science, Deen Dayal Upadhyaya College University of Delhi, Delhi, INDIA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Deen Dayal Upadhyaya College University of Delhi, Delhi, INDIA","institution_ids":["https://openalex.org/I4210095530","https://openalex.org/I110166357"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100605814","display_name":"R. P. Singh","orcid":"https://orcid.org/0000-0002-9896-1170"},"institutions":[{"id":"https://openalex.org/I110166357","display_name":"University of Delhi","ror":"https://ror.org/04gzb2213","country_code":"IN","type":"education","lineage":["https://openalex.org/I110166357"]},{"id":"https://openalex.org/I4210095530","display_name":"Deen Dayal Upadhyay Hospital","ror":"https://ror.org/00n3vh666","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I4210095530"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ram Pal Singh","raw_affiliation_strings":["Department of Computer Science, Deen Dayal Upadhyaya College University of Delhi, Delhi, INDIA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Deen Dayal Upadhyaya College University of Delhi, Delhi, INDIA","institution_ids":["https://openalex.org/I4210095530","https://openalex.org/I110166357"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100747952"],"corresponding_institution_ids":["https://openalex.org/I110166357","https://openalex.org/I4210095530"],"apc_list":null,"apc_paid":null,"fwci":3.7021,"has_fulltext":false,"cited_by_count":46,"citation_normalized_percentile":{"value":0.93329226,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9984999895095825,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.7599893808364868},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7238456010818481},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6565967798233032},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6491515040397644},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6240382790565491},{"id":"https://openalex.org/keywords/horizon","display_name":"Horizon","score":0.6030164957046509},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.48571500182151794},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47556939721107483},{"id":"https://openalex.org/keywords/time-horizon","display_name":"Time horizon","score":0.4531746506690979},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4309841990470886},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4156957268714905},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.400178998708725},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0912354588508606}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7599893808364868},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7238456010818481},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6565967798233032},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6491515040397644},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6240382790565491},{"id":"https://openalex.org/C159176650","wikidata":"https://www.wikidata.org/wiki/Q43261","display_name":"Horizon","level":2,"score":0.6030164957046509},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.48571500182151794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47556939721107483},{"id":"https://openalex.org/C28761237","wikidata":"https://www.wikidata.org/wiki/Q7805321","display_name":"Time horizon","level":2,"score":0.4531746506690979},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4309841990470886},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4156957268714905},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.400178998708725},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0912354588508606},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt45670.2019.8944624","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt45670.2019.8944624","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Responsible consumption and production","score":0.4000000059604645,"id":"https://metadata.un.org/sdg/12"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1560739766","https://openalex.org/W1887565018","https://openalex.org/W1974696541","https://openalex.org/W2012079387","https://openalex.org/W2016166233","https://openalex.org/W2024804972","https://openalex.org/W2026131661","https://openalex.org/W2028072219","https://openalex.org/W2039240409","https://openalex.org/W2055019455","https://openalex.org/W2059776567","https://openalex.org/W2098263691","https://openalex.org/W2102148524","https://openalex.org/W2136939460","https://openalex.org/W2149298154","https://openalex.org/W2159782771","https://openalex.org/W2422659437","https://openalex.org/W2604847698","https://openalex.org/W2803462565","https://openalex.org/W2889194138","https://openalex.org/W2896916590","https://openalex.org/W4236047370","https://openalex.org/W4251197426","https://openalex.org/W6639593163","https://openalex.org/W6680230698"],"related_works":["https://openalex.org/W2906267174","https://openalex.org/W134501823","https://openalex.org/W2013239059","https://openalex.org/W2295626654","https://openalex.org/W4286622840","https://openalex.org/W2536121351","https://openalex.org/W2809608782","https://openalex.org/W2333245620","https://openalex.org/W1889576908","https://openalex.org/W3147633063"],"abstract_inverted_index":{"Multi":[0],"step":[1],"ahead":[2],"(long":[3],"horizon)":[4],"forecasting":[5,79,94,132,159],"in":[6,18,32,127,135],"a":[7,11,44,54,84,96,121],"time":[8,47,77,97,122,165],"series":[9,78,98,123,166],"is":[10,26,103,112,133],"difficult":[12],"task":[13],"for":[14,43,95],"various":[15],"engineering":[16],"applications":[17],"finance,":[19],"geology,":[20],"and":[21,68,91,117],"information":[22],"technology,":[23],"etc.":[24],"It":[25],"observed":[27],"that":[28,151],"different":[29,163],"approaches":[30],"mentioned":[31],"literature":[33],"are":[34],"at":[35],"some":[36],"disadvantage":[37],"to":[38,64,88,114],"give":[39,89,139],"long":[40,92,130],"term":[41],"prediction":[42],"non":[45],"linear":[46],"series.":[48],"Long":[49],"Short-Term":[50],"Memory":[51],"(LSTM)":[52],"network":[53,111],"type":[55],"of":[56,75,120,142],"Recurrent":[57],"Neural":[58],"Network":[59],"(RNN)":[60],"can":[61],"be":[62],"used":[63],"solve":[65],"aforementioned":[66],"problem":[67],"it":[69],"may":[70],"replace":[71],"many":[72],"practical":[73],"implementations":[74],"the":[76],"systems.":[80],"This":[81],"paper":[82,137],"presents":[83],"novel":[85],"LSTM":[86,101,110,154],"model":[87],"short":[90],"horizon":[93,131],"data.":[99],"The":[100],"method":[102,156],"preferable":[104],"over":[105],"other":[106],"existing":[107,148],"algorithms":[108],"as":[109],"able":[113],"learn":[115],"non-linear":[116],"non-stationary":[118],"nature":[119],"which":[124,138],"reduces":[125],"error":[126],"forecasting.":[128],"Also,":[129],"targeted":[134],"this":[136],"wide":[140],"range":[141],"its":[143],"applicability.":[144],"Comparative":[145],"experiments":[146],"with":[147],"models":[149],"demonstrate":[150],"our":[152],"proposed":[153],"ensemble":[155],"achieves":[157],"state-of-the-art":[158],"performance":[160],"on":[161],"four":[162],"real-life":[164],"datasets":[167],"publicly":[168],"available.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":7}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}