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Creation of New Prediction Units in Data Mining System on NetBeans Platform
Havlíček, David ; Bartík, Vladimír (referee) ; Lukáš, Roman (advisor)
The issue of this master's thesis is a creation of new prediction unit for existing system of knowledge discovery in database. The first part of project deal with general problems of knowledge discovery in database and predictive analysis. The second part of the project deal with system developed on FIT, for which is module implemented, used technologies, concept and implementation of mining module for this system. The solution is implemented in Java language and is a built on the NetBeans platform.
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Approximation of spatially-distributed hierarchically organized data
Smejkalová, Veronika ; Žák, Libor (referee) ; Pavlas, Martin (advisor)
The forecast of the waste production is an important information for planning in waste management. The historical data often consists of short time series, therefore traditional prognostic approaches fail. The mathematical model for forecasting of future waste production based on spatially distributed data with hierarchically structure is suggested in this thesis. The approach is based on principles of regression analysis with final balance to ensure the compliance of aggregated data values. The selection of the regression function is a part of mathematical model for high-quality description of data trend. In addition, outlier values are cleared, which occur abundantly in the database. The emphasis is on decomposition of extensive model into subtasks, which lead to a simpler implementation. The output of this thesis is tool tested within case study on municipal waste production data in the Czech Republic.
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Imaging Reflectometry Measuring Thin Films Optical Properties
Běhounek, Tomáš ; Spousta, Jiří (referee) ; Zicha,, Josef (referee) ; Kotačka, Libor (referee) ; Druckmüller, Miloslav (advisor)
V této práci je prezentována inovativní metoda zvaná \textit{Zobrazovací Reflektometrie}, která je založena na principu spektroskopické reflektometrie a je určena pro vyhodnocování optických vlastností tenkých vrstev .\ Spektrum odrazivosti je získáno z map intenzit zaznamenaných CCD kamerou. Každý záznam odpovídá předem nastavené vlnové délce a spektrum odrazivosti může být určeno ve zvoleném bodu nebo ve vybrané oblasti.\ Teoretický model odrazivosti se fituje na naměřená data pomocí Levenberg~-~Marquardtova algoritmu, jehož výsledky jsou optické vlastnosti vrstvy, jejich přesnost, a určení spolehlivosti dosažených výsledků pomocí analýzy citlivosti změn počátečních nastavení optimalizačního algoritmu.
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Confidence regions in nonlinear regression
Marcinko, Tomáš ; Zvára, Karel (advisor) ; Komárek, Arnošt (referee)
The aim of this thesis is a comprehensive description of the properties of a nonlinear least squares estimator for a nonlinear regression model with normally distributed errors and thorough development of various methods for constructing confidence regions and confidence intervals for the parameters of the nonlinear model. Due to the fact that, unlike the case of linear models, there is no easy way to construct an exact confidence region for the parameters, most of these methods are only approximate. A short simulation study comparing observed coverage of various confidence regions and confidence intervals for models with different curvatures and sample sizes is also included. In case of negligible intrinsic curvature the use of likelihood-ratio confidence regions seems the most appropriate.
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On kernel-based nonlinear regression estimation
Kalina, Jan ; Vidnerová, P.
This paper is devoted to two important kernel-based tools of nonlinear regression: the Nadaraya-Watson estimator, which can be characterized as a successful statistical method in various econometric applications, and regularization networks, which represent machine learning tools very rarely used in econometric modeling. This paper recalls both approaches and describes their common features as well as differences. For the Nadaraya-Watsonestimator, we explain its connection to the conditional expectation of the response variable. Our main contribution is numerical analysis of suitable data with an economic motivation and a comparison of the two nonlinear regression tools. Our computations reveal some tools for the Nadaraya-Watson in R software to be unreliable, others not prepared for a routine usage. On the other hand, the regression modeling by means of regularization networks is much simpler and also turns out to be more reliable in our examples. These also bring unique evidence revealing the need for a careful choice of the parameters of regularization networks
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On kernel-based nonlinear regression estimation
Kalina, Jan ; Vidnerová, Petra
This paper is devoted to two important kernel-based tools of nonlinear regression: the Nadaraya-Watson estimator, which can be characterized as a successful statistical method in various econometric applications, and regularization networks, which represent machine learning tools very rarely used in econometric modeling. This paper recalls both approaches and describes their common features as well as differences. For the Nadaraya-Watson estimator, we explain its connection to the conditional expectation of the response variable. Our main contribution is numerical analysis of suitable data with an economic motivation and a comparison of the two nonlinear regression tools. Our computations reveal some tools for the Nadaraya-Watson in R software to be unreliable, others not prepared for a routine usage. On the other hand, the regression modeling by means of regularization networks is much simpler and also turns out to be more reliable in our examples. These also bring unique evidence revealing the need for a careful choice of the parameters of regularization networks
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Technical analysis of financial time series
Faltýnková, Anežka ; Petrásek, Jakub (advisor) ; Hurt, Jan (referee)
The thesis studies the problem of inefficiencies in the finan- cial markets. The first section describes the fundamental concepts, such as the efficient market hypothesis and futures contracts. The necessary mathematics is summarized in the second part, which deals with the link between the futures price and the martingale. The nonlinear regression is introduced and the greatest emphasis is placed on the description of the functional linear model with a scalar response. The main part focuses on the application of this theory. Two models are proposed for predicting prices based on their historical changes. The first model is nonlinear and is based on the assumption that the impact of the price change on the prediction process diminishes exponentially with time. The second one is linear and directly estimates the effect of particular changes. Both models are compared in terms of their ability to predict inefficiencies, calculation costs and stability. 1
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Approximation of spatially-distributed hierarchically organized data
Smejkalová, Veronika ; Žák, Libor (referee) ; Pavlas, Martin (advisor)
The forecast of the waste production is an important information for planning in waste management. The historical data often consists of short time series, therefore traditional prognostic approaches fail. The mathematical model for forecasting of future waste production based on spatially distributed data with hierarchically structure is suggested in this thesis. The approach is based on principles of regression analysis with final balance to ensure the compliance of aggregated data values. The selection of the regression function is a part of mathematical model for high-quality description of data trend. In addition, outlier values are cleared, which occur abundantly in the database. The emphasis is on decomposition of extensive model into subtasks, which lead to a simpler implementation. The output of this thesis is tool tested within case study on municipal waste production data in the Czech Republic.
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Automatic data analysis in capillary zone electrophoresis
Ördögová, Magda ; Dubský, Pavel (advisor) ; Heyda, Jan (referee)
Evaluating data in capillary zone electrophoresis usually involves many steps that require using several different programmes. Apart from evaluating the electrophoreogram itself, it is usual to process the obtained data in some other way. For example, a suitable model is fit to the data in order to obtain physical and chemical parameters of the separation (e.g. stability constant in case of complexation). It is also important to know the accuracy of the evaluation (the calculation error). In this work, new parts of the Eval programme, originally developed for electrophoreogram evaluation, were implemented. The programme now automatically estimates the Haarhoff-van der Linde function (solution of continuity equation in capillary) parameters for analyte peak. Complexing agents are often used to improve the separation in the capillary zone electrophoresis. Complexation in the capillary can be described by its physical and chemical parameters. A new part was added to the Eval programme that allows the user to fit a rectangular hyperbole function to the obtained data. Thus, the regression parameters of this dependence can be gained. The programme can also draw profile diagrams for these parameters, from which the confidence intervals can be read. An option that allows two dependencies to be fitted at...
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Confidence regions in nonlinear regression
Marcinko, Tomáš ; Zvára, Karel (advisor) ; Komárek, Arnošt (referee)
The aim of this thesis is a comprehensive description of the properties of a nonlinear least squares estimator for a nonlinear regression model with normally distributed errors and thorough development of various methods for constructing confidence regions and confidence intervals for the parameters of the nonlinear model. Due to the fact that, unlike the case of linear models, there is no easy way to construct an exact confidence region for the parameters, most of these methods are only approximate. A short simulation study comparing observed coverage of various confidence regions and confidence intervals for models with different curvatures and sample sizes is also included. In case of negligible intrinsic curvature the use of likelihood-ratio confidence regions seems the most appropriate.
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