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Managers use forecasting in budgeting time and resources. In this thesis, various advanced time series models are constructed, computed and tested for adequacy. This thesis serves as a practical guide to regression and time series analysis. It seeks to demonstrate how to approach problems according to scientific standards to students who are familiar with SPSS© but beginners in regression and time series analysis. Bibliographic notes of classical works and more recent academic advances in time series analysis are provided throughout the text. In chapter 4.3 an ordinary least squares regression version of the two models has been computed. In chapter 4.6 a weighted least squares regression has been applied to the models. Autoregressions have been computed in chapter 4.7.1 and two Autoregressive Integrated Moving Average (ARIMA) versions have been constructed in chapter 4.7.6. The various versions of the models have then been compared against each other. The version that fits the data best will be used in forecasting.

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Born on October 26th , 1981 Passed school leaving exam with distinction in July 2000 2003 2007: International Business Studies at FH Kufstein September 2005 July 2006: Exchange study in Dalian, China Currently working for a multinational corporation in China

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