{"id":"https://openalex.org/W4310113562","doi":"https://doi.org/10.1145/3568562.3568637","title":"Solving resource forecasting in WiFi Network by NeuralProphet","display_name":"Solving resource forecasting in WiFi Network by NeuralProphet","publication_year":2022,"publication_date":"2022-11-29","ids":{"openalex":"https://openalex.org/W4310113562","doi":"https://doi.org/10.1145/3568562.3568637"},"language":"en","primary_location":{"id":"doi:10.1145/3568562.3568637","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3568562.3568637","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 11th International Symposium on Information and Communication Technology","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/A5067594887","display_name":"Anh Son Ta","orcid":"https://orcid.org/0000-0001-6009-9741"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Anh Son Ta","raw_affiliation_strings":["Hust, Viet Nam"],"raw_orcid":"https://orcid.org/0000-0001-6009-9741","affiliations":[{"raw_affiliation_string":"Hust, Viet Nam","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025349119","display_name":"Ngoc Bach Pham","orcid":"https://orcid.org/0000-0002-5697-4117"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ngoc Bach Pham","raw_affiliation_strings":["Hust, Viet Nam"],"raw_orcid":"https://orcid.org/0000-0002-5697-4117","affiliations":[{"raw_affiliation_string":"Hust, Viet Nam","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5067594887"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1387,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.56180438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"47","last_page":"51"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9606000185012817,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9606000185012817,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9402999877929688,"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"}},{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.9377999901771545,"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":0.7837529182434082},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.7027498483657837},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6680572032928467},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6011260151863098},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5970626473426819},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5238633155822754},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5192194581031799},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4776795506477356},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.45748037099838257},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.4235004186630249},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3685353100299835},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3554702699184418},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.11174017190933228}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7837529182434082},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.7027498483657837},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6680572032928467},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6011260151863098},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5970626473426819},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5238633155822754},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5192194581031799},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4776795506477356},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.45748037099838257},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.4235004186630249},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3685353100299835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3554702699184418},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.11174017190933228},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3568562.3568637","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3568562.3568637","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 11th International Symposium on Information and Communication Technology","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":2,"referenced_works":["https://openalex.org/W3140570329","https://openalex.org/W4247378692"],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W2390914021","https://openalex.org/W1566614651"],"abstract_inverted_index":{"Time":[0],"series":[1],"forecasting":[2,118],"needs":[3],"several":[4],"approaches":[5],"such":[6],"as":[7],"data":[8,19,24,35,64,78,93],"pretreatment,":[9],"model":[10,88],"construction,":[11],"etc.":[12],"During":[13],"the":[14,18,40,60,63,105,115,119,122,140,144,147],"covid":[15],"19":[16],"outbreak,":[17],"is":[20,37,49,56,99,129],"very":[21],"dynamic,":[22],"therefore":[23],"processing":[25],"and":[26],"appropriate":[27],"modeling":[28],"are":[29],"worried.":[30],"Identifying":[31],"patterns,":[32],"recognizing":[33],"abnormal":[34],"points,":[36],"one":[38],"of":[39,62,80,104,117,121,133],"first":[41],"stages":[42],"to":[43,91],"enhancing":[44],"forecast":[45],"outcomes.":[46,149],"A":[47],"point":[48,53],"considered":[50],"an":[51,71],"anomalous":[52],"when":[54],"it":[55],"far":[57],"distant":[58],"from":[59],"mean":[61],"series.":[65],"In":[66],"this":[67],"research,":[68],"we":[69,85],"deploy":[70],"automated":[72],"anomaly":[73],"detection":[74],"approach":[75],"that":[76,107,132,139],"incorporates":[77],"preparation":[79],"neuralprophet":[81,90],"library.":[82],"After":[83],"that,":[84],"design":[86],"a":[87,102],"via":[89],"predict":[92],"after":[94],"preprocessing":[95],"data.":[96],"The":[97,126],"strategy":[98],"evaluated":[100],"on":[101],"dataset":[103],"times":[106],"public":[108],"wifi":[109],"was":[110],"used":[111],"every":[112],"day":[113],"with":[114,131],"purpose":[116],"value":[120],"following":[123],"30":[124],"days.":[125],"anticipated":[127],"outcome":[128],"compared":[130],"Prophet,":[134],"hybrid":[135],"AR-LSTM,":[136],"consequently":[137],"indicating":[138],"suggested":[141],"technique":[142],"in":[143],"study":[145],"offers":[146],"best":[148]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
