Diploma work is engaged in financial analysis of Stavby silnic a železnic, a.s. company. The aim is to pass judgment on company's present financial situation and its progress from year 2001. It is focused on important facts concerning financial health. It warns of possible threats and try to define some recommendations to achieve stability and prosperity. It utilizes theoretical knowledge of financial analysis like horizontal and vertical analysis of financial statements, rules of financing, ratio indicators, pyramidal system of indicators, working capital analysis, basic economic inequality, EVA method and bankrupt models. Company is also compared with a number of transport building companies.

The graduation thesis "Analysis of stock-exchange data using AI methods" is focused on the use of neural networks while predicating the exchange-rate movements on Change. The theoretical part is divided into three independent units. The Change matters and the related individual terms are described in the first part. In the second part, the two basic approaches to the stock-exchange data analysis are analyzed, these two approaches being the fundamental and technical analysis. The third, and the last, theoretical part forms an individual unit describing the Artificial Intelligence theory. Particularly the issue of the neuronal networks is described in detail. The practical part seeks the use for the chosen neuronal network GAME. It analyses the chosen YMZ9 market. It focuses on the prediction of the exchange-rate movements using the "sliding window" method. The last chapter summarizes the results and it proves that under certain circumstances it is possible to properly use the neuronal networks both for the prediction of the stock-exchange movements and as one of the corner-stones of the profitable trading system.

This thesis focuses on possibility of chaotic (specially economic) time-series prediction. Chaotic time-series are unpredictable in long-term due to their high sensitivity on initial conditions. Nevertheless, their behavior should be more or less predictable in short-term. Goal of this thesis is to show, how much and if any prediction, is possible by non-linear prediction method, and try to reveal or to reject presence of chaotic behavior in them. Work is split into three chapters. Chapter One briefly introduces chosen important concepts and methods from this area. In addition, to describe some prediction methods, there are outlined which indicators and methods are possible to use in order to find possibilities and boundaries of this prediction. Chapter Two is focused on modifications of FracLab software, which is used for create this prediction. Last chapter is experimental. Besides the description of examined time-series and methods, it includes discussion of results.

The diploma thesis is aim at analysis of determinants that determine whether is the market of homes for the elderly and homes with special regime attractive for current and potential entrepreneurs. Theoretical part explains the phenomenom of population ageing, analyzes predictions of population development and the development of number of people suffering from Alzheimer disease, senior's financial resources, reasearches rights and duties of social services providers and analyzes effectiveness of financing the social services according to the type of founder. For needed findings is used analysis of specialized literature and studies. Analytical part analyzes preferences for examined kinds of social services. Offers practical example of running the home with special regime and brings the opinions of specialists on economization of social services. In the end of work are collected findings evaluate by means of SWOT analysis.

In this thesis we apply neural networks as nonparametric and nonlinear methods to the Central European stock markets returns (Czech, Polish, Hungarian and German) modelling. In the first two chapters we define prediction task and link the classical econometric analysis to neural networks. We also present optimization methods which will be used in the tests, conjugate gradient, Levenberg-Marquardt, and evolutionary search method. Further on, we present statistical methods for comparing the predictive accuracy of the non-nested models, as well as economic significance measures. In the empirical tests we first show the power of neural networks on Mackey-Glass chaotic time series followed by real-world data of the daily and weekly returns of mentioned stock exchanges for the 2000:2006 period. We find neural networks to have significantly lower prediction error than classical models for daily DAX series, weekly PX50 and BUX series. The lags of time-series were used, and also cross-country predictability has been tested, but the results were not significantly different. We also achieved economic significance of predictions with both daily and weekly PX-50, BUX and DAX with 60% accuracy of prediction. Finally we use neural network to learn Black-Scholes model and compared the pricing errors of...

Diploma thesis is focused on problematics of artificial neural networks and their usage on capital markets. There is a software created as a part of this diploma thesis which can load input data and create neural network that serves for share price forecast. This program is created in numerical computing environment MATLAB. Created neural network is tested under simulation of business model. Results are discussed upon examination of results of simulation.

This master’s thesis deals with the evaluation of the financial situation of BS vinařské potřeby, s.r.o., and proposals for its improvement. Analyses used in this master’s thesis are processed by the methods of financial and strategic analyses and also mathematical and statistical methods from period 2011-2015. The results of this master’s thesis evaluate investment from the economical effectiveness.

In this thesis we try using machine learning techniques to predict final grade of a student in a learning management system on the basis of his behavior during the semester. The aim is to determine the optimal technology for the extraction, treatment and machine learning on data. The whole system would then be implemented as a module that we will be able to plug in the existing system.

The main goal of this thesis was to get acquainted with the phases of data mining, with the support of the programming languages Python and R in the field of data mining and demonstration of their use in two case studies. The comparison of these languages in the field of data mining is also included. The data preprocessing phase and the mining algorithms for classification, prediction and clustering are described here. There are illustrated the most significant libraries for Python and R. In the first case study, work with time series was demonstrated using the ARIMA model and Neural Networks with precision verification using a Mean Square Error. In the second case study, the results of football matches are classificated using the K - Nearest Neighbors, Bayes Classifier, Random Forest and Logical Regression. The precision of the classification is displayed using Accuracy Score and Confusion Matrix. The work is concluded with the evaluation of the achived results and suggestions for the future improvement of the individual models.

This thesis summarizes latest findings about municipal solid waste (MSW) modelling. These are used to solve multivariable version of inverse prediction problem. It is not possible to solve such problem analytically, so heuristic framework using regression models and data reconciliation was developed. As a side product, models for MSW modelling using PCA (Principal Component Analysis) and LM (Linear Model) were created. These were compared with heuristic model called RF (Random Forest). Both of these models were also used for per capita MSW modelling. Theoretical parts about generalized linear models, data reconciliation and nonlinear programming are also included.