{"id":"https://openalex.org/W2558544472","doi":"https://doi.org/10.1109/cec.2016.7744231","title":"Feature engineering for improving financial derivatives-based rainfall prediction","display_name":"Feature engineering for improving financial derivatives-based rainfall prediction","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2558544472","doi":"https://doi.org/10.1109/cec.2016.7744231","mag":"2558544472"},"language":"en","primary_location":{"id":"doi:10.1109/cec.2016.7744231","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2016.7744231","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Congress on Evolutionary Computation (CEC)","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/A5026676925","display_name":"Sam Cramer","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sam Cramer","raw_affiliation_strings":["School of Computing University of Kent"],"affiliations":[{"raw_affiliation_string":"School of Computing University of Kent","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087235995","display_name":"Michael Kampouridis","orcid":"https://orcid.org/0000-0003-0047-7565"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Kampouridis","raw_affiliation_strings":["School of Computing University of Kent"],"affiliations":[{"raw_affiliation_string":"School of Computing University of Kent","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087201377","display_name":"Alex A. Freitas","orcid":"https://orcid.org/0000-0001-9825-4700"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alex A. Freitas","raw_affiliation_strings":["School of Computing University of Kent"],"affiliations":[{"raw_affiliation_string":"School of Computing University of Kent","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026676925"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5709,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.92099691,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"7","issue":null,"first_page":"3483","last_page":"3490"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9994999766349792,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9994999766349792,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9940000176429749,"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/feature-engineering","display_name":"Feature engineering","score":0.8102646470069885},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.8085037469863892},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7290468215942383},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6733498573303223},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6679916381835938},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5455490946769714},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49425840377807617},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4847673773765564},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.47518861293792725},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4699910879135132},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46256929636001587},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4602130949497223},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4151630401611328},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13875794410705566}],"concepts":[{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.8102646470069885},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.8085037469863892},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7290468215942383},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6733498573303223},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6679916381835938},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5455490946769714},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49425840377807617},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4847673773765564},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.47518861293792725},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4699910879135132},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46256929636001587},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4602130949497223},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4151630401611328},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13875794410705566},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cec.2016.7744231","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2016.7744231","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"},{"id":"pmh:oai:kar.kent.ac.uk:55153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2016.7744231>)","pdf_url":null,"source":{"id":"https://openalex.org/S4377196264","display_name":"Kent Academic Repository (University of Kent)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I20581793","host_organization_name":"University of Kent","host_organization_lineage":["https://openalex.org/I20581793"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/6","display_name":"Clean water and sanitation","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1124582155","https://openalex.org/W1565746575","https://openalex.org/W1970156874","https://openalex.org/W1979156432","https://openalex.org/W1981751431","https://openalex.org/W1997211160","https://openalex.org/W2008313676","https://openalex.org/W2027649639","https://openalex.org/W2081097085","https://openalex.org/W2094125108","https://openalex.org/W2129999366","https://openalex.org/W2135448789","https://openalex.org/W2158638608","https://openalex.org/W2245959749","https://openalex.org/W3103913776","https://openalex.org/W6679742958"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175"],"abstract_inverted_index":{"Rainfall":[0],"is":[1,27,32,45,76,178],"one":[2],"of":[3,56,73,104,147,168,211],"the":[4,54,67,71,102,108,122,139,145,148,174,209],"most":[5],"challenging":[6],"variables":[7,124],"to":[8,117,133,138,179],"predict,":[9],"as":[10,192],"it":[11],"exhibits":[12],"very":[13],"unique":[14],"characteristics":[15],"that":[16,36,197],"do":[17],"not":[18],"exist":[19],"in":[20,47,66,198],"other":[21],"time":[22],"series":[23],"data.":[24],"Moreover,":[25],"rainfall":[26,57,63,68,75,87,109],"a":[28,83,193,206],"major":[29],"component":[30],"and":[31,142,172],"essential":[33],"for":[34,62],"applications":[35],"surround":[37],"water":[38],"resource":[39],"planning.":[40],"In":[41,90],"particular,":[42],"this":[43,91],"paper":[44,92],"interested":[46],"extending":[48],"previous":[49,149],"work":[50],"carried":[51],"out":[52],"on":[53,107,163],"prediction":[55,88,110],"using":[58,82,160,201],"Genetic":[59],"Programming":[60],"(GP)":[61],"derivatives.":[64],"Currently":[65],"derivatives":[69],"literature,":[70],"process":[72],"predicting":[74],"dominated":[77],"by":[78,99],"statistical":[79],"models,":[80],"namely":[81],"Markov-chain":[84],"extended":[85],"with":[86],"(MCRP).":[89],"we":[93,131,143],"further":[94,134],"extend":[95],"our":[96,136,157],"new":[97,158],"methodology":[98],"looking":[100],"at":[101],"effect":[103],"feature":[105,128,161],"engineering":[106,113,129,162],"process.":[111],"Feature":[112],"will":[114],"allow":[115],"us":[116],"extract":[118],"additional":[119],"information":[120],"from":[121],"data":[123,166],"created.":[125],"By":[126],"incorporating":[127],"techniques":[130],"look":[132],"tailor":[135],"GP":[137,159,182,200,207],"problem":[140],"domain":[141],"compare":[144],"performance":[146],"GP,":[150],"which":[151,190],"previously":[152],"statistically":[153],"outperformed":[154],"MCRP,":[155],"against":[156],"21":[164],"different":[165],"sets":[167],"cities":[169],"across":[170],"Europe":[171],"report":[173],"results.":[175],"The":[176],"goal":[177],"see":[180],"whether":[181],"can":[183],"outperform":[184],"its":[185],"predecessor":[186],"without":[187,208],"extra":[188,202,212],"features,":[189],"acts":[191],"benchmark.":[194],"Results":[195],"indicate":[196],"general":[199],"features":[203],"significantly":[204],"outperforms":[205],"use":[210],"features.":[213]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}