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Forecasting: Methods and Applications

dc.contributor.authorMakridakis, Spyros
dc.date.accessioned2015-12-11T10:55:22Z
dc.date.available2015-12-11T10:55:22Z
dc.date.issued2002
dc.identifier.isbn0-471-53233-9
dc.identifier.issn0169-2070
dc.identifier.urihttp://hdl.handle.net/11728/6581
dc.description.abstractThis book covers what the authors call the “full range of major forecasting methods.” These comprise of the traditional time series methods of decomposition, exponential smoothing, simple and multiple linear regression and Box-Jenkins’ ARIMA models. Further to those, this 3rd edition very wisely includes some more advanced forecasting methods such as dynamic regression, neural networks, state space modeling as well as some “new ideas for combining statistical and judgmental forecasting” amongst others. A modern approach to long-term forecasting based on mega trends, analogies and scenarios is also includeden_UK
dc.language.isoenen_UK
dc.publisherJohn Wiley and Sonsen_UK
dc.relation.ispartofseriesInternational Journal of Forecasting;
dc.rightsc John Wiley and Sonsen_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.subjectResearch Subject Categories::SOCIAL SCIENCES::Business and economicsen_UK
dc.titleForecasting: Methods and Applicationsen_UK
dc.typeBook chapteren_UK


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Except where otherwise noted, this item's license is described as c John Wiley and Sons