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Devon K. Barrow
Researcher at University of Birmingham
Publications - 17
Citations - 1053
Devon K. Barrow is an academic researcher from University of Birmingham. The author has contributed to research in topics: Time series & Model selection. The author has an hindex of 14, co-authored 17 publications receiving 733 citations. Previous affiliations of Devon K. Barrow include Lancaster University & Coventry University.
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Neural network ensemble operators for time series forecasting
TL;DR: The proposed mode ensemble operator is found to produce the most accurate forecasts, followed by the median, while the mean has relatively poor performance, suggesting that the mode operator should be considered as an alternative to the mean and median operators in forecasting applications.
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Forecasting: theory and practice
Fotios Petropoulos,Daniele Apiletti,Vassilios Assimakopoulos,Mohamed Zied Babai,Devon K. Barrow,Souhaib Ben Taieb,Christoph Bergmeir,Ricardo J. Bessa,Jakub Bijak,John E. Boylan,Jethro Browell,Claudio Carnevale,Jennifer L. Castle,Pasquale Cirillo,Michael P. Clements,Clara Cordeiro,Clara Cordeiro,Fernando Luiz Cyrino Oliveira,Shari De Baets,Alexander Dokumentov,Joanne Ellison,Piotr Fiszeder,Philip Hans Franses,David T. Frazier,Michael Gilliland,M. Sinan Gönül,Paul Goodwin,Luigi Grossi,Yael Grushka-Cockayne,Mariangela Guidolin,Massimo Guidolin,Ulrich Gunter,Xiaojia Guo,Renato Guseo,Nigel Harvey,David F. Hendry,Ross Hollyman,Tim Januschowski,Jooyoung Jeon,Victor Richmond R. Jose,Yanfei Kang,Anne B. Koehler,Stephan Kolassa,Nikolaos Kourentzes,Nikolaos Kourentzes,Sonia Leva,Feng Li,Konstantia Litsiou,Spyros Makridakis,Gael M. Martin,Andrew B. Martinez,Andrew B. Martinez,Sheik Meeran,Theodore Modis,Konstantinos Nikolopoulos,Dilek Önkal,Alessia Paccagnini,Alessia Paccagnini,Anastasios Panagiotelis,Ioannis P. Panapakidis,Jose M. Pavía,Manuela Pedio,Manuela Pedio,Diego J. Pedregal,Pierre Pinson,Patrícia Ramos,David E. Rapach,J. James Reade,Bahman Rostami-Tabar,Michał Rubaszek,Georgios Sermpinis,Han Lin Shang,Evangelos Spiliotis,Aris A. Syntetos,Priyanga Dilini Talagala,Thiyanga S. Talagala,Len Tashman,Dimitrios D. Thomakos,Thordis L. Thorarinsdottir,Ezio Todini,Juan Ramón Trapero Arenas,Xiaoqian Wang,Robert L. Winkler,Alisa Yusupova,Florian Ziel +84 more
TL;DR: A non-systematic review of the theory and the practice of forecasting, offering a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts.
Journal ArticleDOI
The effect of positive feedback in a constraint-based intelligent tutoring system
TL;DR: Empirical evaluation shows that students who were interacting with the augmented version of SQL-Tutor learned at twice the speed as the students who interacted with the standard, error feedback only, version of the system.
Journal ArticleDOI
Another look at forecast selection and combination: evidence from forecast pooling
TL;DR: A heuristic is proposed to automatically identify forecast pools, irrespective of their source or the performance criteria, and demonstrate that in various conditions it performs at least as good as alternative pools that require additional modelling decisions and better than selection or combination.
Journal ArticleDOI
Distributions of forecasting errors of forecast combinations: Implications for inventory management
TL;DR: In this paper, the authors examined the forecast error distributions of base and combination forecasts and their implications for inventory performance, and found that combining multiple forecasts is effective not only in reducing forecast errors, but also in being less sensitive to limitations of a single model.