{"id":"https://openalex.org/W47788662","doi":null,"title":"Composite Forecasting: Combining Forecasts for Improved Accuracy","display_name":"Composite Forecasting: Combining Forecasts for Improved Accuracy","publication_year":2000,"publication_date":"2000-07-01","ids":{"openalex":"https://openalex.org/W47788662","mag":"47788662"},"language":"en","primary_location":{"id":"mag:47788662","doi":null,"is_oa":false,"landing_page_url":"https://www.questia.com/library/journal/1P3-57920416/composite-forecasting-combining-forecasts-for-improved","pdf_url":null,"source":{"id":"https://openalex.org/S2764593516","display_name":"The Journal of Business Forecasting Methods & Systems","issn_l":"1930-126X","issn":["1930-126X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"The Journal of Business Forecasting Methods & Systems","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5036036445","display_name":"Charles W. Chase","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Charles W. Chase","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5036036445"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.00978569,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":95},"biblio":{"volume":"19","issue":"2","first_page":"2","last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":1.0,"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":1.0,"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/T11674","display_name":"Sports Analytics and Performance","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/forecast-combination","display_name":"forecast combination","score":0.8769999742507935},{"id":"https://openalex.org/keywords/forecast-accuracy","display_name":"forecast accuracy","score":0.6629999876022339},{"id":"https://openalex.org/keywords/weighting-methods","display_name":"weighting schemes","score":0.7379999756813049},{"id":"https://openalex.org/keywords/forecast-bias","display_name":"forecast bias","score":0.5019999742507935},{"id":"https://openalex.org/keywords/long-term-forecasting","display_name":"long-term forecasting","score":0.7350000143051147},{"id":"https://openalex.org/keywords/uncertainty","display_name":"uncertainty","score":0.5440000295639038},{"id":"https://openalex.org/keywords/simple-model","display_name":"simple model","score":0.33799999952316284},{"id":"https://openalex.org/keywords/sports-forecasting","display_name":"sports prediction","score":0.33399999141693115},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"weather forecasting","score":0.4390000104904175},{"id":"https://openalex.org/keywords/financial-forecasting","display_name":"financial forecasting","score":0.6809999942779541},{"id":"https://openalex.org/keywords/economic-forecasting","display_name":"economic forecasting","score":0.5109999775886536},{"id":"https://openalex.org/keywords/educational-forecasting","display_name":"educational forecasting","score":0.46799999475479126},{"id":"https://openalex.org/keywords/history-of-forecasting","display_name":"history of forecasting","score":0.6259999871253967},{"id":"https://openalex.org/keywords/winkler","display_name":"Winkler","score":0.3869999945163727},{"id":"https://openalex.org/keywords/levin","display_name":"Levin","score":0.4830000102519989},{"id":"https://openalex.org/keywords/gordon","display_name":"Gordon","score":0.4740000069141388}],"concepts":[{"id":"https://openalex.org/C120954023","wikidata":"https://www.wikidata.org/wiki/Q1127277","display_name":"Consensus forecast","level":2,"score":0.8200030326843262},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6088730096817017},{"id":"https://openalex.org/C170061395","wikidata":"https://www.wikidata.org/wiki/Q5468164","display_name":"Forecast skill","level":2,"score":0.5850300788879395},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4900032579898834},{"id":"https://openalex.org/C2781303124","wikidata":"https://www.wikidata.org/wiki/Q4834681","display_name":"BATES","level":2,"score":0.44754305481910706},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3681010603904724},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3631604313850403},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.30141186714172363},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29543614387512207},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14666405320167542},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:47788662","doi":null,"is_oa":false,"landing_page_url":"https://www.questia.com/library/journal/1P3-57920416/composite-forecasting-combining-forecasts-for-improved","pdf_url":null,"source":{"id":"https://openalex.org/S2764593516","display_name":"The Journal of Business Forecasting Methods & Systems","issn_l":"1930-126X","issn":["1930-126X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"The Journal of Business Forecasting Methods & Systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7095863365","https://openalex.org/W247765774","https://openalex.org/W7101060618","https://openalex.org/W4245349857","https://openalex.org/W1999656559","https://openalex.org/W2158592301","https://openalex.org/W329012419","https://openalex.org/W2484942060","https://openalex.org/W1588320899","https://openalex.org/W1606573484"],"abstract_inverted_index":{"The":[0,64,156,279],"thought":[1],"process":[2],"around":[3],"composite":[4,51,69],"forecasting":[5,70],"in":[6,169,198,258,400,575,616],"not":[7,334],"a":[8,50,72,95,104,185,213,245,295,323,336,346,373,416,598],"new":[9],"one.":[10],"It":[11],"has":[12,240,322],"been":[13,241],"studied":[14],"for":[15,79,181,221,237,272],"years":[16],"with":[17,471],"empirical":[18],"evidence":[19,414],"that":[20,124,162,171,282,369,382,394,474,537,591],"indicates":[21],"the":[22,42,46,88,99,114,126,134,148,154,172,175,210,231,262,273,313,316,356,360,370,384,395,430,464,472,479,493,511,538,549,558,561,567,572,581,606],"combination":[23,214],"of":[24,48,68,74,90,108,158,200,208,212,215,233,247,261,264,301,315,339,349,359,365,372,383,397,429,463,515,524,540,551,554,560,569,571,600],"forecast":[25,32,52,235,375,411,583],"methods":[26,176,191,216,236,499,528,603],"tend":[27],"to":[28,56,112,129,138,152,166,194,298,312,325,500,521],"outperform":[29],"most":[30,65,387],"single":[31],"methods.":[33,556],"A":[34],"good":[35,380],"example":[36],"is":[37,71,128,161,164,226,353],"Gordon":[38],"(1924),":[39],"who":[40,76,113,328],"conducted":[41],"earliest":[43],"research":[44,458],"on":[45,205,230],"value":[47,232,289],"using":[49,189,517,597],"by":[53,415,459,596],"asking":[54],"people":[55],"estimate":[57],"things":[58],"like":[59,84,529],"distance,":[60],"volume,":[61],"or":[62,304,332],"weight.":[63],"common":[66],"form":[67],"group":[73],"judges":[75,93],"assign":[77],"scores":[78,89,102,286],"athletes":[80],"at":[81,94],"sporting":[82,366],"events":[83,367],"boxing.":[85],"By":[86],"averaging":[87],"any":[91],"three":[92],"given":[96],"boxing":[97],"event,":[98],"obtained":[100],"combined":[101,142,374,398,461,506,527,552,562,582],"produce":[103,130],"more":[105,196,621],"fair":[106],"chance":[107],"being":[109],"correct":[110],"as":[111,294,343,379,381,403,620],"actual":[115],"winner":[116],"should":[117,136,144,434],"be.":[118],"Bates":[119,452],"and":[120,277,284,290,386,408,448,453,532,534,584,604,609],"Granger":[121,454],"(1969)":[122,455],"suggested":[123,421],"if":[125,478],"objective":[127],"accurate":[131],"forecasts,":[132,276],"then":[133,449],"analyst":[135],"attempt":[137],"combine":[139,522],"forecasts.":[140,362,525,607],"Those":[141],"forecasts":[143,150,160,168,187,352,399,462,480,507,516,544,553,563,622],"be":[145,195,292,594],"preferred":[146],"over":[147],"individual":[149,543],"used":[151,293,496],"generate":[153],"composite.":[155],"notion":[157,207],"combining":[159,234,351,605],"it":[163],"better":[165,354],"average":[167,357,450],"hope":[170],"biases":[173],"among":[174,441],"and/or":[177],"forecasters":[178,300,327,344],"will":[179],"compensate":[180],"one":[182,426,433,502,578],"another.":[183],"As":[184,341],"result,":[186],"developed":[188,509],"different":[190,255,484,490,498,505],"are":[192,329,476,481],"expected":[193],"useful":[197],"cases":[199],"high":[201],"uncertainty.":[202],"To":[203],"expand":[204],"this":[206],"uncertainty,":[209],"use":[211,396,444],"would":[217,443],"seem":[218],"especially":[219],"relevant":[220],"longrange":[222],"forecasting,":[223],"where":[224,580],"uncertainty":[225],"extremely":[227],"high.":[228],"Evidence":[229],"improved":[238],"accuracy":[239,271,285,310,358,371,617],"overwhelming,":[242],"coming":[243],"from":[244,510],"variety":[246],"areas.":[248],"In":[249,318],"fact,":[250],"Winkler":[251,610],"(1967,":[252],"1971)":[253],"examined":[254],"weighting":[256,321,519],"schemes":[257,520],"his":[259],"study":[260],"predication":[263],"football":[265],"scores.":[266],"He":[267],"compared":[268],"equal":[269],"ratings,":[270],"preceding":[274],"week's":[275],"self-ratings.":[278],"conclusions":[280],"indicated":[281],"self-ratings":[283,303],"have":[287,305,392],"modest":[288],"can":[291,593],"screening":[296],"device":[297],"eliminate":[299,326],"low":[302],"demonstrated":[306],"very":[307],"poor":[308],"previous":[309],"relative":[311],"rest":[314],"group.":[317],"other":[319],"words,":[320],"tendency":[324],"either":[330,570],"inexperienced":[331],"do":[333],"possess":[335,345],"minimum":[337,347],"level":[338,348],"expertise.":[340],"long":[342],"expertise,":[350],"than":[355,424,487,548,566],"component":[361,586],"These":[363,588],"studies":[364,391],"found":[368,536,613],"was":[376],"almost":[377],"always":[378],"best":[385,585],"experienced":[388],"forecaster.":[389],"Additional":[390],"shown":[393],"areas":[401],"such":[402],"finance,":[404],"psychology,":[405],"education,":[406],"economics,":[407],"weather":[409],"improve":[410],"accuracy.":[412],"Further":[413],"biologist":[417],"named":[418],"Levin":[419],"(1966)":[420],"that,":[422,440],"rather":[423,486],"building":[425,436],"master":[427],"model":[428],"real":[431],"world,":[432],"consider":[435],"several":[437],"simpler":[438],"models":[439],"them,":[442],"all":[445,576],"available":[446],"data,":[447],"them.":[451],"followed":[456],"Levin's":[457],"testing":[460],"international":[465],"air":[466],"travel":[467],"market.":[468],"They":[469,495,526],"started":[470],"premise":[473],"improvements":[475],"greatest":[477],"based":[482],"upon":[483,489],"information,":[485],"merely":[488],"assumptions":[491],"about":[492],"relationships.":[494],"five":[497,512],"obtain":[501],"forecast.":[503],"Nine":[504],"were":[508,545,564,618,623],"original":[513],"sets":[514],"various":[518],"pairs":[523],"exponential":[530],"smoothing":[531],"BoxJenkins,":[533],"they":[535],"error":[539,550,568],"these":[541],"two":[542,573],"much":[546],"higher":[547],"both":[555],"Overall,":[557],"errors":[559],"smaller":[565],"components":[574],"but":[577],"case,":[579],"tied.":[587],"findings":[589],"imply":[590],"gains":[592,615],"achieved":[595,619],"set":[599],"simple":[601],"extrapolation":[602],"Makridakis":[608],"(1983)":[611],"also":[612],"significant":[614],"combined.":[624],"\u2026":[625]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2016-06-24T00:00:00"}