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Forecasting demand of magazines and modelling seasonality

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dc.contributor Graduate Program in Industrial Engineering.
dc.contributor.advisor Hörmann, Wolfgang.
dc.contributor.author Aktel, Abdullah.
dc.date.accessioned 2023-03-16T10:27:57Z
dc.date.available 2023-03-16T10:27:57Z
dc.date.issued 2008.
dc.identifier.other IE 2008 A38
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/13208
dc.description.abstract The main aim of this thesis is to find a sensible way to model the seasonality and forecast the demand of magazines automatically. Demand forecasting in magazine industry is very complex and historic delivery and sale data are often short, unstable and particularly perturbed by numerous factors. Generating forecasts from these large numbers of time series requires some degree of automation and simple forecasting models. The first part of the thesis explains basic forecasting notions. Especially, the need for an automatic forecasting system is emphasized and the steps of automatic forecasting study are explained. Also, a statistical analysis is done to decide the suitable smoothing model alternatives. Finally, initialization and parameter optimization procedures are discussed. In the second part, demand estimation and handling of the censored demand in case of sellout is analyzed. In addition, the two main strategies used for planning are mentioned: topdown and bottom-up. The third part presents new forecasting methods based on combining forecasts and grouping similar characteristic endpoints by using real data. The last part explains the data organization and calculation of MAD and lost sales by using R and describes the some important algorithms that are used in magazine forecasting. .
dc.format.extent 30cm.
dc.publisher Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2008.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Newspaper and periodical wholesalers.
dc.subject.lcsh Sales reporting.
dc.title Forecasting demand of magazines and modelling seasonality
dc.format.pages xii, 77 leaves;


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