{"id":"https://openalex.org/W2883621141","doi":"https://doi.org/10.1145/3230905.3230946","title":"Feedforward and Recurrent Neural Networks for Time Series Forecasting","display_name":"Feedforward and Recurrent Neural Networks for Time Series Forecasting","publication_year":2018,"publication_date":"2018-05-02","ids":{"openalex":"https://openalex.org/W2883621141","doi":"https://doi.org/10.1145/3230905.3230946","mag":"2883621141"},"language":"en","primary_location":{"id":"doi:10.1145/3230905.3230946","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3230905.3230946","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications","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/A5075030531","display_name":"Rohaifa Khaldi","orcid":"https://orcid.org/0000-0001-6224-2206"},"institutions":[{"id":"https://openalex.org/I126477371","display_name":"Mohammed V University","ror":"https://ror.org/00r8w8f84","country_code":"MA","type":"education","lineage":["https://openalex.org/I126477371"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Rohaifa Khaldi","raw_affiliation_strings":["ENSIAS, Mohammed V University, Rabat, Morocco"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ENSIAS, Mohammed V University, Rabat, Morocco","institution_ids":["https://openalex.org/I126477371"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018862939","display_name":"Raddouane Chiheb","orcid":"https://orcid.org/0000-0002-9576-2686"},"institutions":[{"id":"https://openalex.org/I126477371","display_name":"Mohammed V University","ror":"https://ror.org/00r8w8f84","country_code":"MA","type":"education","lineage":["https://openalex.org/I126477371"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Raddouane Chiheb","raw_affiliation_strings":["ENSIAS, Mohammed V University, Rabat, Morocco"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ENSIAS, Mohammed V University, Rabat, Morocco","institution_ids":["https://openalex.org/I126477371"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020368196","display_name":"Abdellatif El Afia","orcid":"https://orcid.org/0000-0003-1921-4431"},"institutions":[{"id":"https://openalex.org/I126477371","display_name":"Mohammed V University","ror":"https://ror.org/00r8w8f84","country_code":"MA","type":"education","lineage":["https://openalex.org/I126477371"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Abdellatif El Afia","raw_affiliation_strings":["ENSIAS, Mohammed V University, Rabat, Morocco"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ENSIAS, Mohammed V University, Rabat, Morocco","institution_ids":["https://openalex.org/I126477371"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I126477371"],"apc_list":null,"apc_paid":null,"fwci":1.5413,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.8473654,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"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":0.9976999759674072,"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":0.9976999759674072,"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":0.9958000183105469,"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":0.9901000261306763,"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/feed-forward","display_name":"Feed forward","score":0.7104103565216064},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.7047613859176636},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.7022595405578613},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6997221112251282},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6935262680053711},{"id":"https://openalex.org/keywords/feedforward-neural-network","display_name":"Feedforward neural network","score":0.6876922845840454},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.648634135723114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.506468653678894},{"id":"https://openalex.org/keywords/seasonality","display_name":"Seasonality","score":0.4977720081806183},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.45952826738357544},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.406879723072052},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3305497169494629},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08382794260978699}],"concepts":[{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.7104103565216064},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.7047613859176636},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.7022595405578613},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6997221112251282},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6935262680053711},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.6876922845840454},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.648634135723114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.506468653678894},{"id":"https://openalex.org/C125403950","wikidata":"https://www.wikidata.org/wiki/Q2111082","display_name":"Seasonality","level":2,"score":0.4977720081806183},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.45952826738357544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.406879723072052},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3305497169494629},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08382794260978699},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"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/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3230905.3230946","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3230905.3230946","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications","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":24,"referenced_works":["https://openalex.org/W638887343","https://openalex.org/W1967885203","https://openalex.org/W1968975103","https://openalex.org/W1971335110","https://openalex.org/W1971906504","https://openalex.org/W2009334577","https://openalex.org/W2011227258","https://openalex.org/W2040395995","https://openalex.org/W2067183857","https://openalex.org/W2107071461","https://openalex.org/W2132393939","https://openalex.org/W2137983211","https://openalex.org/W2175143722","https://openalex.org/W2485309236","https://openalex.org/W2500779354","https://openalex.org/W2548334553","https://openalex.org/W2731837149","https://openalex.org/W2741660982","https://openalex.org/W2741969331","https://openalex.org/W2782413309","https://openalex.org/W2967372818","https://openalex.org/W3146803896","https://openalex.org/W4242522087","https://openalex.org/W4242661553"],"related_works":["https://openalex.org/W2115072676","https://openalex.org/W4311212821","https://openalex.org/W2045727192","https://openalex.org/W1529660427","https://openalex.org/W2102065768","https://openalex.org/W4390752998","https://openalex.org/W2524120878","https://openalex.org/W2794343888","https://openalex.org/W2158578859","https://openalex.org/W2109916967"],"abstract_inverted_index":{"This":[0],"study":[1,67],"aims":[2],"at":[3],"examining":[4],"and":[5,16,53,55,110],"comparing":[6],"the":[7,33,63,73],"ability":[8],"of":[9,22,65,81],"ANNs":[10],"variances,":[11],"including":[12],"MLP,":[13],"RBFNN,":[14],"ELMAN":[15,70,90],"JORDAN":[17],"in":[18,76],"forecasting":[19,98],"monthly":[20],"data":[21],"four":[23],"different":[24],"time":[25,46,82,96],"series":[26,47,83,97],"patterns.":[27,84],"As":[28],"well":[29],"as":[30],"to":[31,95,103],"criticize":[32],"concept":[34],"asserting":[35],"that":[36,69,89],"MLP":[37],"is":[38,92],"a":[39],"\"universal":[40],"approximator\".":[41],"The":[42],"tackled":[43],"patterns":[44],"include":[45],"with":[48,78],"seasonality,":[49],"trend,":[50,54],"combined":[51],"seasonality":[52],"non-constant":[56],"variance.":[57],"Thereafter,":[58],"based":[59],"on":[60],"statistical":[61],"metrics,":[62],"results":[64],"this":[66],"showed":[68],"network":[71,91],"outperforms":[72],"remaining":[74],"models":[75],"dealing":[77],"all":[79],"types":[80],"Therefore,":[85],"we":[86],"can":[87],"confirm":[88],"more":[93],"suited":[94],"than":[99],"feedforward":[100],"networks,":[101],"thanks":[102],"its":[104,108],"embedding":[105],"memory":[106],"between":[107],"input":[109],"hidden":[111],"layers.":[112]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":6}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
