B
Bahman Rostami-Tabar
Researcher at Cardiff University
Publications - 29
Citations - 447
Bahman Rostami-Tabar is an academic researcher from Cardiff University. The author has contributed to research in topics: Demand forecasting & Computer science. The author has an hindex of 8, co-authored 20 publications receiving 236 citations. Previous affiliations of Bahman Rostami-Tabar include Coventry University & École Centrale Paris.
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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.
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Demand forecasting by temporal aggregation
TL;DR: In this paper, the authors investigate the impact of temporal aggregation on forecasting performance and show that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant, and the process parameters.
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Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?
TL;DR: In this paper, two different schools of thought have emerged: one focuses on identifying a single optimal temporal aggregation level at which a forecasting model maximises its accuracy, and the second approach fits multiple models at multiple levels, each capable of capturing different features of the data.
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Forecasting COVID-19 daily cases using phone call data
TL;DR: In this article, a simple multiple linear regression model was proposed to forecast the number of daily confirmed cases in COVID-19 related variables, and a probabilistic forecast that allows decision makers to better deal with risk.
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A novel ranking procedure for forecasting approaches using Data Envelopment Analysis
TL;DR: In this article, a multiplicative Data Envelopment Analysis (DEA) model was proposed to compare the accuracy of different forecasting approaches to rank several forecasting techniques. But in practice there are some situations where different error measures yield different decisions on forecasting approach selection.