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Time series prediction
Boková, Kateřina ; Pilát, Martin (advisor) ; Koubková, Alena (referee)
In this present work, we provide an overview of methods for time series modelling and prediction. We describe methods based on decomposition as well as methods based on the Box-Jenkins methodology. Moreover, we also discuss methods based on the ideas from computational intelligence -mainly neural networks. Thedescription of the methods is focused on the algorithmic aspects -we derive the ways in which the parameters of the models are set. The work also contains a software, which allows the user to apply the described methods to given time series and compare them among each other.
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Statistical model of the face shape
Boková, Kateřina ; Pelikán, Josef (advisor) ; Krajíček, Václav (referee)
The goal of this thesis is to use machine learning methods for datasets of scanned faces and to create a program that allows to explore and edit faces represented as triangle meshes with a number of controls. Firstly we had to reduce dimension of triangle meshes by PCA and then we tried to predict shape of meshes according to physical properties like weight, height, age and BMI. The modeled faces can be used in animation or games.
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Statistical model of the face shape
Boková, Kateřina ; Pelikán, Josef (advisor) ; Krajíček, Václav (referee)
The goal of this thesis is to use machine learning methods for datasets of scanned faces and to create a program that allows to explore and edit faces represented as triangle meshes with a number of controls. Firstly we had to reduce dimension of triangle meshes by PCA and then we tried to predict shape of meshes according to physical properties like weight, height, age and BMI. The modeled faces can be used in animation or games.
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|
|
Time series prediction
Boková, Kateřina ; Pilát, Martin (advisor) ; Koubková, Alena (referee)
In this present work, we provide an overview of methods for time series modelling and prediction. We describe methods based on decomposition as well as methods based on the Box-Jenkins methodology. Moreover, we also discuss methods based on the ideas from computational intelligence -mainly neural networks. Thedescription of the methods is focused on the algorithmic aspects -we derive the ways in which the parameters of the models are set. The work also contains a software, which allows the user to apply the described methods to given time series and compare them among each other.
|
|
|
Time series prediction
Boková, Kateřina ; Pilát, Martin (advisor) ; Koubková, Alena (referee)
In this present work, we provide an overview of methods for time series modelling and prediction. We describe methods based on decomposition as well as methods based on the Box-Jenkins methodology. Moreover, we also discuss methods based on the ideas from computational intelligence -mainly neural networks. Thedescription of the methods is focused on the algorithmic aspects -we derive the ways in which the parameters of the models are set. The work also contains a software, which allows the user to apply the described methods to given time series and compare them among each other.
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