{"id":"https://openalex.org/W4324267416","doi":"https://doi.org/10.1109/fmec57183.2022.10062720","title":"Evaluating Amazon EC2 Spot Price Prediction Models Using Regression Error Characteristic Curve","display_name":"Evaluating Amazon EC2 Spot Price Prediction Models Using Regression Error Characteristic Curve","publication_year":2022,"publication_date":"2022-12-12","ids":{"openalex":"https://openalex.org/W4324267416","doi":"https://doi.org/10.1109/fmec57183.2022.10062720"},"language":"en","primary_location":{"id":"doi:10.1109/fmec57183.2022.10062720","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fmec57183.2022.10062720","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5024939272","display_name":"Batool Alkaddah","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Batool Alkaddah","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada","Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072134441","display_name":"Anjali Agarwal","orcid":"https://orcid.org/0000-0003-3639-3304"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Anjali Agarwal","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada","Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.998199999332428,"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":0.998199999332428,"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/T11182","display_name":"Auction Theory and Applications","score":0.992900013923645,"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":0.9854999780654907,"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":0.6471996307373047},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6418508291244507},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5893441438674927},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5237400531768799},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46637871861457825},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4638080596923828},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44969117641448975},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.44347095489501953},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4299662113189697},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.37551212310791016},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21372857689857483}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6471996307373047},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6418508291244507},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5893441438674927},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5237400531768799},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46637871861457825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4638080596923828},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44969117641448975},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.44347095489501953},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4299662113189697},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37551212310791016},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21372857689857483},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/fmec57183.2022.10062720","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fmec57183.2022.10062720","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","raw_type":"proceedings-article"},{"id":"pmh:oai:https://spectrum.library.concordia.ca:992011","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400871","display_name":"Spectrum Research Repository (Concordia University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I60158472","host_organization_name":"Concordia University","host_organization_lineage":["https://openalex.org/I60158472"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Thesis"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1974799041","https://openalex.org/W2032927332","https://openalex.org/W2080562691","https://openalex.org/W2097998348","https://openalex.org/W2101234009","https://openalex.org/W2140190241","https://openalex.org/W2153635508","https://openalex.org/W2158698691","https://openalex.org/W2295598076","https://openalex.org/W2297152540","https://openalex.org/W2490662969","https://openalex.org/W2560674852","https://openalex.org/W2775270516","https://openalex.org/W2801697832","https://openalex.org/W2808080170","https://openalex.org/W2898702671","https://openalex.org/W2910895903","https://openalex.org/W2911964244","https://openalex.org/W3048150824","https://openalex.org/W3105640742","https://openalex.org/W3166877859","https://openalex.org/W3175020838","https://openalex.org/W4393791620","https://openalex.org/W6674385629","https://openalex.org/W6682354853"],"related_works":["https://openalex.org/W31220157","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W4401154840","https://openalex.org/W4318612353","https://openalex.org/W2165884543","https://openalex.org/W3186837933","https://openalex.org/W2368989808","https://openalex.org/W1995617853"],"abstract_inverted_index":{"Amazon":[0],"EC2":[1,63],"offers":[2],"inactive":[3],"virtual":[4],"machines":[5],"(VM)":[6],"as":[7,155,210],"spot":[8,62],"instances":[9],"at":[10],"up":[11],"to":[12,25,42,138,170,192,196],"90%":[13],"discount.":[14],"In":[15,52,115],"return,":[16],"the":[17,22,46,57,69,85,97,102,107,119,123,130,133,147,167,172,177,183,198,205],"least":[18],"expensive":[19],"option":[20],"requires":[21],"customers'":[23],"usage":[24],"be":[26],"tolerated":[27],"with":[28,65],"a":[29,211],"low":[30],"availability":[31],"level":[32],"agreement.":[33],"Thus,":[34],"many":[35],"studies":[36],"proposed":[37],"forecasting":[38,72,98],"and":[39,77,113,129,160,208],"prediction":[40],"mechanisms":[41],"asses":[43],"in":[44,60,106,136],"finding":[45],"best":[47],"set":[48],"of":[49,71,87],"maximum":[50],"prices.":[51],"this":[53,194],"paper,":[54],"we":[55,91,117],"study":[56],"model's":[58],"efficiency":[59],"predicting":[61],"prices":[64],"focusing":[66],"on":[67],"assessing":[68],"performance":[70,213],"algorithms:":[73],"RFR,":[74],"XGBoost,":[75],"k-NNR,":[76],"SVR.":[78],"Model's":[79],"evaluation":[80],"is":[81,190],"crucial":[82],"for":[83,95,182,215],"measuring":[84],"accuracy":[86],"predicted":[88],"prices,":[89],"thus,":[90],"select":[92],"six":[93],"metrics":[94,105],"evaluating":[96,216],"results.":[99],"We":[100],"used":[101],"top":[103],"implemented":[104],"related":[108],"work:":[109],"MAPE,":[110],"RMSE,":[111],"MAE,":[112],"MSE.":[114],"addition,":[116],"assessed":[118],"spotted":[120],"models":[121,217],"using":[122],"Regression":[124],"Error":[125],"Characteristics":[126],"(REC)":[127],"curve":[128,134,207],"Area":[131],"under":[132],"(AUC-REC)":[135],"comparison":[137],"prior":[139],"measures.":[140],"Three":[141],"aspects":[142],"are":[143],"considered":[144],"while":[145],"building":[146],"models:":[148],"dataset":[149],"time":[150],"per":[151],"year,":[152],"training":[153],"window":[154],"1-day":[156],"or":[157],"1-month":[158],"ahead":[159],"instance":[161],"location.":[162],"The":[163],"trained":[164],"model":[165],"applies":[166],"cross-validation":[168],"technique":[169,195],"learn":[171],"ideal":[173],"hyper-parameters":[174],"that":[175],"achieve":[176],"highest":[178],"accuracy.":[179,200],"However,":[180],"except":[181],"SVR":[184],"model,":[185],"our":[186],"findings":[187],"indicate":[188],"it":[189],"unnecessary":[191],"use":[193],"improve":[197],"algorithms'":[199],"Our":[201],"results":[202],"investigations":[203],"display":[204],"REC":[206],"AUC-REC":[209],"superior":[212],"measurements":[214],"over":[218],"different":[219],"accuracy-loss":[220],"thresholds.":[221]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
