Do we want coherent hierarchical forecasts, or minimal MAPEs or MAEs? (We won't get both!)
In: International journal of forecasting, Band 39, Heft 4, S. 1512-1517
ISSN: 0169-2070
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In: International journal of forecasting, Band 39, Heft 4, S. 1512-1517
ISSN: 0169-2070
In: International journal of forecasting, Band 38, Heft 4, S. 1562-1568
ISSN: 0169-2070
In: International journal of forecasting, Band 36, Heft 1, S. 208-211
ISSN: 0169-2070
In: International journal of forecasting, Band 34, Heft 4, S. 830-831
ISSN: 0169-2070
In: International journal of forecasting, Band 34, Heft 2, S. 312-313
ISSN: 0169-2070
In: International journal of forecasting, Band 33, Heft 3, S. 743-744
ISSN: 0169-2070
In: International journal of forecasting, Band 32, Heft 3, S. 788-803
ISSN: 0169-2070
In: International journal of forecasting, Band 27, Heft 2, S. 631-633
ISSN: 0169-2070
In: International journal of forecasting, Band 27, Heft 2, S. 238-251
ISSN: 0169-2070
In: International journal of forecasting, Band 38, Heft 4, S. 1283-1318
ISSN: 0169-2070
In: International journal of forecasting, Band 38, Heft 4, S. 1319-1324
ISSN: 0169-2070
"This book surveys what executives who make decisions based on forecasts and professionals responsible for forecasts should know about forecasting. It discusses how individuals and firms should think about forecasting and guidelines for good practices. It introduces readers to the subject of time series, presents basic and advanced forecasting models, from exponential smoothing across ARIMA to modern Machine Learning methods, and examines human judgment's role in interpreting numbers and identifying forecasting errors and how it should be integrated into organizations. This is a great book to start learning about forecasting if you are new to the area or have some preliminary exposure to forecasting. Whether you are a practitioner, either in a role managing a forecasting team or at operationally involved in demand planning, a software designer, a student or an academic teaching business analytics, operational research, or operations management courses, the book can inspire you to rethink demand forecasting. No prior knowledge of higher mathematics, statistics, operations research, or forecasting is assumed in this book. It is designed to serve as a first introduction to the non-expert who needs to be familiar with the broad outlines of forecasting without specializing in it. This may include a manager overseeing a forecasting group, or a student enrolled in an MBA program, an executive education course, or programs not specialising in analytics. Worked examples accompany the key formulae to show how they can be implemented"--