{"id":"https://openalex.org/W3049032160","doi":"https://doi.org/10.3390/make2030014","title":"Impact of Uncertainty in the Input Variables and Model Parameters on Predictions of a Long Short Term Memory (LSTM) Based Sales Forecasting Model","display_name":"Impact of Uncertainty in the Input Variables and Model Parameters on Predictions of a Long Short Term Memory (LSTM) Based Sales Forecasting Model","publication_year":2020,"publication_date":"2020-08-15","ids":{"openalex":"https://openalex.org/W3049032160","doi":"https://doi.org/10.3390/make2030014","mag":"3049032160"},"language":"en","primary_location":{"id":"doi:10.3390/make2030014","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2030014","pdf_url":"https://www.mdpi.com/2504-4990/2/3/14/pdf?version=1597488303","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/2/3/14/pdf?version=1597488303","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112725597","display_name":"S. GOEL","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shakti Goel","raw_affiliation_strings":["Chief Data and Analytics Officer, TBO Holidays, TEK Travels, Gurugram, Haryana 122022, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chief Data and Analytics Officer, TBO Holidays, TEK Travels, Gurugram, Haryana 122022, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076406369","display_name":"Rahul Bajpai","orcid":"https://orcid.org/0000-0001-9848-0783"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahul Bajpai","raw_affiliation_strings":["Senior Machine Learning Engineer, TBO Holidays, TEK Travels, Gurugram, Haryana 122022, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Senior Machine Learning Engineer, TBO Holidays, TEK Travels, Gurugram, Haryana 122022, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112725597"],"corresponding_institution_ids":[],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":1.3012,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.82505856,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"2","issue":"3","first_page":"256","last_page":"270"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9979000091552734,"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":0.9979000091552734,"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":0.9968000054359436,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9710000157356262,"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/gumbel-distribution","display_name":"Gumbel distribution","score":0.675684928894043},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.6681538820266724},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6052432060241699},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5887265801429749},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5503813028335571},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4860951900482178},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.47094643115997314},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.45887696743011475},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4040379822254181},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4031737148761749},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1335328221321106},{"id":"https://openalex.org/keywords/extreme-value-theory","display_name":"Extreme value theory","score":0.12446284294128418}],"concepts":[{"id":"https://openalex.org/C137610916","wikidata":"https://www.wikidata.org/wiki/Q1096862","display_name":"Gumbel distribution","level":3,"score":0.675684928894043},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.6681538820266724},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6052432060241699},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5887265801429749},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5503813028335571},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4860951900482178},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.47094643115997314},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.45887696743011475},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4040379822254181},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4031737148761749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1335328221321106},{"id":"https://openalex.org/C147581598","wikidata":"https://www.wikidata.org/wiki/Q729429","display_name":"Extreme value theory","level":2,"score":0.12446284294128418},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/make2030014","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2030014","pdf_url":"https://www.mdpi.com/2504-4990/2/3/14/pdf?version=1597488303","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:56374f75e9d3490f9d35f808df89e74a","is_oa":true,"landing_page_url":"https://doaj.org/article/56374f75e9d3490f9d35f808df89e74a","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":"Machine Learning and Knowledge Extraction, Vol 2, Iss 3, Pp 256-270 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2504-4990/2/3/14/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/make2030014","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/make2030014","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2030014","pdf_url":"https://www.mdpi.com/2504-4990/2/3/14/pdf?version=1597488303","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3049032160.pdf","grobid_xml":"https://content.openalex.org/works/W3049032160.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1494192115","https://openalex.org/W1810943226","https://openalex.org/W1964175594","https://openalex.org/W2001557392","https://openalex.org/W2005821748","https://openalex.org/W2014928429","https://openalex.org/W2066554066","https://openalex.org/W2074466695","https://openalex.org/W2119512810","https://openalex.org/W2130737351","https://openalex.org/W2257784575","https://openalex.org/W2511084971","https://openalex.org/W2530867817","https://openalex.org/W2578837221","https://openalex.org/W2597704325","https://openalex.org/W2604313474","https://openalex.org/W2735251667","https://openalex.org/W2742666152","https://openalex.org/W2751802138","https://openalex.org/W2758567761","https://openalex.org/W2787832669","https://openalex.org/W2803204740","https://openalex.org/W2807414627","https://openalex.org/W2907345037","https://openalex.org/W2909877301","https://openalex.org/W2910798665","https://openalex.org/W2943008967","https://openalex.org/W2955914872","https://openalex.org/W2963653111","https://openalex.org/W2966757765","https://openalex.org/W2980678560","https://openalex.org/W3033731332","https://openalex.org/W3035130140","https://openalex.org/W3092984679","https://openalex.org/W4205110562","https://openalex.org/W4244698247","https://openalex.org/W4249179470","https://openalex.org/W4397289120","https://openalex.org/W6732281541","https://openalex.org/W6765555761","https://openalex.org/W6867249476"],"related_works":["https://openalex.org/W2115040659","https://openalex.org/W2392757156","https://openalex.org/W3121924949","https://openalex.org/W4210771670","https://openalex.org/W2951988075","https://openalex.org/W2270643620","https://openalex.org/W1570428685","https://openalex.org/W2083778309","https://openalex.org/W1498453022","https://openalex.org/W1578916557"],"abstract_inverted_index":{"A":[0],"Long":[1],"Short":[2],"Term":[3],"Memory":[4],"(LSTM)":[5],"based":[6],"sales":[7,16,49,110,143,203,328],"model":[8,29,37,83,125,207,278,286,363],"has":[9],"been":[10],"developed":[11],"to":[12,35,44,84,108,123,151,189,200,206,212,246,276,301,345,361,374],"forecast":[13],"the":[14,36,51,56,68,78,86,101,109,127,132,137,152,155,161,185,190,201,213,234,247,251,265,269,277,303,317,322,334,340,354,364],"global":[15,266,309],"of":[17,20,55,60,80,88,95,115,129,139,154,157,163,181,187,192,259,268,305,333],"hotel":[18],"business":[19],"Travel":[21],"Boutique":[22],"Online":[23],"Holidays":[24],"(TBO":[25],"Holidays).":[26],"The":[27,93,113,215,291,336],"LSTM":[28,82,293,323,343],"is":[30,65,98,103,223,281],"a":[31,45,116,313],"multivariate":[32],"model;":[33],"input":[34,57,107,121],"includes":[38],"several":[39],"independent":[40],"variables":[41,204],"in":[42,142,296,339,353,366],"addition":[43,186],"dependent":[46],"variable,":[47,99,321],"viz.,":[48],"from":[50],"previous":[52],"step.":[53],"One":[54,257],"variables,":[58],"\u201cnumber":[59],"active":[61,89,96,158,193],"bookers":[62,90,97,194],"per":[63,91],"day\u201d,":[64],"estimated":[66],"for":[67,75,145,225,238],"same":[69],"day":[70],"as":[71,105,119,170,197,199,357],"sales.":[72,310,367],"This":[73,134],"need":[74],"estimation":[76,153],"requires":[77],"development":[79],"another":[81,124],"predict":[85,326],"number":[87,94,156,191],"day.":[92],"so":[100],"predicted":[102,117],"used":[104,295,300,352,373],"an":[106,120],"forecasting":[111],"model.":[112],"use":[114],"variable":[118,122,315],"increases":[126],"chance":[128],"uncertainty":[130,188,261],"entering":[131],"system.":[133],"paper":[135,341],"discusses":[136],"quantum":[138],"variability":[140],"observed":[141],"predictions":[144,182,208,222,236],"various":[146,376],"uncertainties":[147,226],"or":[148],"noise":[149,167,240,243],"due":[150],"bookers.":[159],"For":[160],"purposes":[162],"this":[164,260],"study,":[165],"different":[166],"distributions":[168,175,230],"such":[169],"normalized,":[171],"uniform,":[172],"and":[173,221,284],"logistic":[174],"are":[176,210,288,298,359],"used,":[177],"among":[178],"others.":[179],"Analyses":[180],"demonstrate":[183],"that":[184,209,264,349],"via":[195],"dropouts":[196],"well":[198],"lagged":[202],"leads":[205],"close":[211],"observations.":[214],"least":[216,270],"squared":[217,271],"error":[218],"between":[219],"observations":[220],"higher":[224],"modeled":[227],"using":[228],"other":[229],"(without":[231],"dropouts)":[232],"with":[233,274],"worst":[235],"being":[237],"Gumbel":[239],"distribution.":[241],"Gaussian":[242],"added":[244],"directly":[245],"weights":[248],"matrix":[249,280],"yields":[250],"best":[252],"results":[253],"(minimum":[254],"prediction":[255],"errors).":[256],"possibility":[258],"could":[262],"be":[263,346,351,371],"minimum":[267],"objective":[272],"function":[273],"respect":[275],"weight":[279],"not":[282,289],"reached,":[283],"therefore,":[285],"parameters":[287],"optimal.":[290],"two":[292],"models":[294,324,344],"series":[297],"also":[299],"study":[302],"impact":[304,320],"corona":[306,318],"virus":[307,319],"on":[308],"By":[311],"introducing":[312],"new":[314],"called":[316],"can":[325,350,370],"corona-affected":[327],"within":[329],"five":[330],"percent":[331],"(5%)":[332],"actuals.":[335],"research":[337],"discussed":[338],"finds":[342],"effective":[347],"tools":[348,369],"travel":[355],"industry":[356],"they":[358],"able":[360],"successfully":[362],"trends":[365],"These":[368],"reliably":[372],"simulate":[375],"hypothetical":[377],"scenarios":[378],"also.":[379]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
