6533b870fe1ef96bd12cf14a
RESEARCH PRODUCT
Forecasting : theory and practice
Fotios PetropoulosDaniele ApilettiVassilios AssimakopoulosMohamed Zied BabaiDevon K. BarrowSouhaib Ben TaiebChristoph BergmeirRicardo J. BessaJakub BijakJohn E. BoylanJethro BrowellClaudio CarnevaleJennifer L. CastlePasquale CirilloMichael P. ClementsClara CordeiroFernando Luiz Cyrino OliveiraShari De BaetsAlexander DokumentovJoanne EllisonPiotr FiszederPhilip Hans FransesDavid T. FrazierMichael GillilandM. Sinan GönülPaul GoodwinLuigi GrossiYael Grushka-cockayneMariangela GuidolinMassimo GuidolinUlrich GunterXiaojia GuoRenato GuseoNigel HarveyDavid F. HendryRoss HollymanTim JanuschowskiJooyoung JeonVictor Richmond R. JoseYanfei KangAnne B. KoehlerStephan KolassaNikolaos KourentzesSonia LevaFeng LiKonstantia LitsiouSpyros MakridakisGael M. MartinAndrew B. MartinezSheik MeeranTheodore ModisKonstantinos NikolopoulosDilek ÖNkalAlessia PaccagniniAnastasios PanagiotelisIoannis PanapakidisJose M. PavíaManuela PedioDiego J. PedregalPierre PinsonPatrícia RamosDavid E. RapachJ. James ReadeBahman Rostami-tabarMichał RubaszekGeorgios SermpinisHan Lin ShangEvangelos SpiliotisAris A. SyntetosPriyanga Dilini TalagalaThiyanga S. TalagalaLen TashmanDimitrios ThomakosThordis ThorarinsdottirEzio TodiniJuan Ramón Trapero ArenasXiaoqian WangRobert L. WinklerAlisa YusupovaFlorian Zielsubject
FOS: Computer and information sciencesComputer Science - Machine LearningTime seriesEconomicsApplicationOther Engineering and Technologies not elsewhere specifiedEconometrics (econ.EM)HAMethodMachine Learning (stat.ML)ReviewStatistics - ApplicationsMachine Learning (cs.LG)FOS: Economics and businessBusiness and EconomicsStatistics - Machine LearningMethodsPrincipleREVIEWApplications (stat.AP)Övrig annan teknikN100Business and International ManagementNationalekonomiEconomics - EconometricsBusiness AdministrationFöretagsekonomiAPPLICATIONSOther Statistics (stat.OT)Wirtschaftswissenschaftenstat.OTStatistics - Other StatisticsComputer Science - Learning003: SystemePRINCIPLESecon.EMApplicationsMETHODSStatistics - Applications; Statistics - Applications; Computer Science - Learning; econ.EM; Statistics - Machine Learning; stat.OTEncyclopediaPredictionPrinciplesREVIEW ENCYCLOPEDIA METHODS APPLICATIONS PRINCIPLES TIME SERIES PREDICTIONForecastingdescription
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)001 Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)307403/2019-0 CA19130 ECO2017-87245-R SBPLY/19/180501/000151 info:eu-repo/semantics/publishedVersion
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2022-07-01 |