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Special aspects of non-linear time series modelling
Studnička, Václav ; Zichová, Jitka (advisor) ; Hudecová, Šárka (referee)
Various models, such as ARMA and GARCH, are used in the financial time series framework. The purpose of this thesis is to present an alternative for these models which are bilinear time series models. First chapter is theore- tical, there is a short introduction to the theory of time series and ARMA models. Second chapter focuses on theoretical aspects of the simple bilinear model, third chapter presents the theory for general bilinear model in the similiar fashion as for simple model. Last chapter is focused on practical aspects, it contains simulations and examines the properties of estimates based on the presented theory, final part is devoted to the comparison of properties of ARMA models and bilinear models for selected financial data. 1
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Neural Networks in R
Arzumanov, Eduard ; Bašta, Milan (advisor) ; Žižka, David (referee)
The aim of this work was to present the issue of neural network, which is still, despite the fact it exist and has been applied for several years, remains quite unknown for a considerably big part of public and academical environment. The aim of the practical part was to verify via practical application if neural network are truly a better instrument of statistical analysis, than the commonly used ones, especially when the goal is to analyze and describe complex processes and relationships between them. Further aim of the work was to investigate and describe the relationships between the development of trading volumes of Apple shares and the shares of competitive companies regarding the market of smart phones such as Google, HTC, Nokia, Samsung using neural network models. The attainment of these goals was realized through a rather extensive description of neural networks theory as well as the presentation of valuable theoretical tools for avoiding the frequent barriers occurring during the practical implementation. This practical application was realized via software called R, which has widely spread lately due to its availability and a vast range of flexibility, which is provided to users. The value of this work is familiarization and the creation of an integrated knowledge within readers about the issue of neural networks and the deliverance of a proof, that neural networks are indeed a better tool compared to the commonly used ones (ARMA models, linear regression). The author of the work gained a lot of useful knowledge about neural networks, learned how to use them in practice especially in the environment of R software, by which he shifted his proficiency with the current software to a whole new level.
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