The aim of this thesis is to briefly introduce the reader to the problem of development of the electricity consumption, to show the possibilities of its prediction, and provide an example of electricity consumption analysis, which may serve as a basis for long-term forecast. The first part of this work is devoted to brief overview of the development of consumption of electrical energy in the Czech Republic and factors associated with that development. The main events of Czech electrification history are outlined, as well as factors influencing the magnitude of the demand for electricity in the recent times. There are described the possibilities of the influence of foreign exchange and price of the electricity, net losses, GDP, the consumption of gas, the number of inhabitants, or the structure of the economy. This work further describes the development of the consumption of electric energy abroad and comparison of the electricity consumption in the Czech Republic to its neighboring countries, in which there are apparently influence by variety of factors that affect the consumption development trend. For more complex overview, the electricity consumption of most of European states is being noted, and near the end of the chapter the development in poorest countries and in quickly growing economies is shown. The fourth chapter mentions methods for short and middle-term prediction of consumption of electricity. There also is more consistent description of possibilities useful for long-term prediction, for which the use of the results of statistics analysis is possible. The description of observed data that are used to find the dependences of different factors is made in the following part. Further the approach of the statistics analysis used in this thesis is shown, and the key terms are explained. The dates concerning the consumption of Czech Republic are analyzed, followed by the states with similar trend of the consumption and finally other European states. Sixth chapter examines the possible uses of the outcomes of statistical analyses for long-term prediction of electricity consumption. The conclusion sums up the knowledge acquired during the research concerning problem of electricity consumption and my own analysis of data.

Master's thesis deals with vehicles collisions within an environment of logistic distribution storehous. The aim is to avoid potential collisions that can occur between these vehicles. In the first chapter are detailed collisions of particles and elements impact for the law of conservation of energy and momentum. In further sections is carried out detailed research of elastic and inelastic collisions in an ideal environment, which are the theoretical basis for the simulation of the real environment. Collisions are supported by detailed calculations and added pictures. The second chapter deals with the development of double-layer application simulating elastic collisions in an ideal environment. In subchapters are descriptions of the classes and methods for computing prediction of collisions with examples of source code. The last section describes the software platform JavaFX and used animation classes that are required for visualization issues. The last chapter, which is the main goal of the work is done by the expansion of the existing design environment of storehouse and vehicles. Particular chapters then describe the cases of various types of collisions their detection and avoidance. There is an application designed to address this issue, with a description of the classes and methods. Used methods are supported with flowcharts for easier understanding.

This project explores basic methods of time series analysis and decomposition of these series using the additive model. It describes creation of classes for generating and decomposition of time series in Python. This project also guides the reader through creation of Matlab user interface which is used to generate time series and mark chosen parameters. I also go through implementation of functions for time series decomposition previously created in Python. I chose seven parameters of which I kept track. I also chose general features for representing chosen parameters as well as features which were chosen carefully for each parameter. Every time series generated by this user interface are then used to train a program, which classifies them for semantic description. After training the created model was used to predict chosen parameters of previously unknown time series.

This diploma thesis deals with the theoretical analysis of the predictive methods of signal interpolation and signal modeling using sinusoidal model. On the basis of this theory the algorithm for the reconstruction of the missing sections in the audio signal is implemented in computing environment MATLAB. Results of mass testing reconstructions are displayed using objective methods SNR and PEMO-Q. Further experiments are carried out on single signals and their evaluation is described.

This master's thesis deals with a design of backup power system, which will be using renewable energy sources, particularly solar energy. The first part describes the potential of solar power plant in the Czech Republic. The next parts describe types of photovoltaic systems, their components, design of photovoltaic systems and possibilities of controlling power consumption. Due to the fluctuating supply from renewable energy sources the thesis also deals with possibilities of predicting of the production electricity from these sources. In the practical part the design of backup power system for the house is done, which will also be working in summer as an optimizer for own consumption. A financial evaluation was done for this proposal.

RNA secondary structure is very important in many biological processes. Efficient structure prediction can give information for experimental investigations of these processes. Many available programs for secondary structure prediction exist. Some of them use single sequence, the others use more related sequences. Pseudoknots are still problematic for most methods. This work presents several methods and publicly available software and the implementation of minimum free energy method is described.

The goal of this thesis is to study the possibilities of Artificial neural network as an innovative mathematical methods for financial analysis of company performance, to find out what are today´s requests for performance evaluation of companies are and to identify possible ways how to use this relatively new concept in this area. When processing the possibilities of the computer program Maple for mathematical calculations will be applied. Intermediate objectives are then acquainted with the basic principle on which the artificial neural networks works, to analyze the financial performance of specific company and evaluate potential predictive abilities of the proposed network. The result of the work should be evaluating the success of this approach to financial analysis and evaluation of its use in practice.

This Master's thesis focuses on applying artificial intelligence tools for the prediction of development financial markets. Major emphasis is placed on evaluating the usability of neural networks to determine the prediction in the foreign exchange markets. It is also provided suggestion for fully automated processing of market data and subsequent submitting of trading orders.

The purpose of this master’s thesis is comparison of selected methods for prediction of the capital markets. It is about technical analysis methods, used to predict trend of share prices of the capital market and finding entry signals to buy stock and exit sinals to sell stock. Methods are analyzed for selected stock from SPAD, which is part of Prague Stock Exchange.

The diploma thesis evaluates economic status of selected company by analysis of main financial indicators. The analysis is made by regression analysis method and forecast of time series is made by using selected regression function. The thesis result is evaluation of executed analysis and list of recommendations for improvement of the current economic company status.