{"id":"https://openalex.org/W3200254011","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533963","title":"A Survey on Classical and Deep Learning based Intermittent Time Series Forecasting Methods","display_name":"A Survey on Classical and Deep Learning based Intermittent Time Series Forecasting Methods","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200254011","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533963","mag":"3200254011"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533963","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533963","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5048829909","display_name":"R Karthikeswaren","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Karthikeswaren R","raw_affiliation_strings":["AI Garage, Mastercard, Gurgaon, India"],"affiliations":[{"raw_affiliation_string":"AI Garage, Mastercard, Gurgaon, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089264766","display_name":"Kanishka Kayathwal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kanishka Kayathwal","raw_affiliation_strings":["AI Garage, Mastercard, Gurgaon, India"],"affiliations":[{"raw_affiliation_string":"AI Garage, Mastercard, Gurgaon, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075372536","display_name":"Gaurav Dhama","orcid":"https://orcid.org/0000-0003-1092-5771"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gaurav Dhama","raw_affiliation_strings":["AI Garage, Mastercard, Gurgaon, India"],"affiliations":[{"raw_affiliation_string":"AI Garage, Mastercard, Gurgaon, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059891327","display_name":"Ankur Arora","orcid":"https://orcid.org/0000-0003-4350-5773"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ankur Arora","raw_affiliation_strings":["AI Garage, Mastercard, Gurgaon, India"],"affiliations":[{"raw_affiliation_string":"AI Garage, Mastercard, Gurgaon, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5048829909"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5381,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.69619255,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9995999932289124,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9995999932289124,"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/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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9872000217437744,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.757725715637207},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.6759210824966431},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6533600091934204},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6183902621269226},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5451549291610718},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5368204116821289},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5143300294876099},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5074750781059265},{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.4472362995147705},{"id":"https://openalex.org/keywords/supply-chain-management","display_name":"Supply chain management","score":0.41512390971183777},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3499715328216553},{"id":"https://openalex.org/keywords/industrial-engineering","display_name":"Industrial engineering","score":0.3341791331768036},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.30720067024230957},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10525980591773987}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.757725715637207},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.6759210824966431},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6533600091934204},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6183902621269226},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5451549291610718},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5368204116821289},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5143300294876099},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5074750781059265},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.4472362995147705},{"id":"https://openalex.org/C44104985","wikidata":"https://www.wikidata.org/wiki/Q492886","display_name":"Supply chain management","level":3,"score":0.41512390971183777},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3499715328216553},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.3341791331768036},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.30720067024230957},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10525980591773987},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533963","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533963","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5099999904632568,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1485443335","https://openalex.org/W1563720478","https://openalex.org/W1972835575","https://openalex.org/W1983757845","https://openalex.org/W1989787309","https://openalex.org/W2010284576","https://openalex.org/W2061277123","https://openalex.org/W2095220782","https://openalex.org/W2101906966","https://openalex.org/W2114733835","https://openalex.org/W2443555980","https://openalex.org/W2541929603","https://openalex.org/W2607045400","https://openalex.org/W2771000365","https://openalex.org/W2804304893","https://openalex.org/W2885761374","https://openalex.org/W2952278188","https://openalex.org/W2990432009","https://openalex.org/W3007066689","https://openalex.org/W3042623101","https://openalex.org/W3151524951","https://openalex.org/W4200370185","https://openalex.org/W4206173445","https://openalex.org/W4297789729","https://openalex.org/W4302438404","https://openalex.org/W4399636024","https://openalex.org/W6780872688"],"related_works":["https://openalex.org/W2767550285","https://openalex.org/W2620085874","https://openalex.org/W2064496565","https://openalex.org/W1575661125","https://openalex.org/W4309070544","https://openalex.org/W2108330697","https://openalex.org/W2983500849","https://openalex.org/W3024999678","https://openalex.org/W388184414","https://openalex.org/W2026811664"],"abstract_inverted_index":{"Demand":[0],"forecasting":[1,21,35,79,87,179],"is":[2],"a":[3,137,156],"fundamental":[4],"aspect":[5],"of":[6,17,128,141],"inventory":[7],"and":[8,66,109,144,150,170],"supply":[9],"chain":[10],"management.":[11],"Due":[12],"to":[13,46,57,72,83,124,174],"the":[14,18,77,126,129,163,167,178],"sporadic":[15],"nature":[16],"demand,":[19],"demand":[20],"involves":[22],"dealing":[23],"with":[24,59,132],"intermittent":[25,42,60,88],"time":[26,43,61],"series":[27,44,62],"in":[28,49,101,111],"domains":[29],"such":[30,50,63],"as":[31,64,152],"retail,":[32],"manufacturing.":[33],"Conventional":[34],"methods":[36,56,80,96,105,131],"do":[37],"not":[38],"work":[39,70,84],"well":[40,85],"for":[41,86],"due":[45],"inherent":[47],"sparsity":[48],"series.":[51,89],"Researchers":[52],"have":[53,91,98,173],"proposed":[54,100],"multiple":[55],"deal":[58],"Croston":[65],"its":[67],"variants.":[68],"Our":[69],"aims":[71,160],"provide":[73,155],"an":[74],"insight":[75],"into":[76],"various":[78],"traditionally":[81],"known":[82],"We":[90,154],"also":[92],"explored":[93],"deep":[94,133],"learning":[95,134],"that":[97,159,172],"been":[99,148],"recent":[102],"literature.":[103],"These":[104],"are":[106,117],"thoroughly":[107],"reviewed":[108],"explained":[110],"this":[112],"survey":[113],"paper.":[114],"Additionally,":[115],"experiments":[116],"done":[118],"on":[119],"two":[120],"publicly":[121],"available":[122],"datasets":[123],"compare":[125],"performance":[127],"traditional":[130],"models.":[135],"Furthermore,":[136],"hybrid":[138],"model":[139],"made":[140],"independent":[142],"classification":[143],"regression":[145],"trees":[146],"has":[147],"implemented":[149],"studied":[151],"well.":[153],"comprehensive":[157],"evaluation":[158],"at":[161],"selecting":[162],"appropriate":[164],"method,":[165],"given":[166],"dataset,":[168],"context,":[169],"objectives":[171],"be":[175],"met":[176],"by":[177],"practitioner/researcher.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
