The main goal of this thesis is to analyse the fluctuation of the employees in a well established Czech manufacturing company. Due to the GDPR regulations, the underlying company is kept anonymised in this thesis. The data were transformed into longitudinal data and the GEE methodology was used for the analysis of the fluctuation. In the first chapter, an introduction to the problem and a short description of the data is provided. The second chapter contains some theoretical description of the GEE methodology and the QIC information criterion. In the third chapter, multiple models for a binary and multinomial response are fitted to the data and their results are described in detail. This allows us to describe the influence of various factors to the fluctuation of the employees in the underlying company. 1

The topic of this thesis is to solve spatial heterogenity in forestry models by means of utilizing linear mixed-effects models (LMM) and geographically weighted regression (GWR) to model a height-diameter curve. Both of these methods were previously tested, and they have a high potential to reduce the minimal necessary amount of data needed, and at the same time, increase precision. The data come from VŠLP Křtiny, LÚ Borky, a complex of forests utilized for educational purposes by Mendel’s university in Brno. We choosed beech as the model species. We split the data into training and validation sets for fitting, and consequent prediction assessment. Resulting models were compared with OLS fitted global model. Local OLS models were unreliable, as only a very few measured trees were available for each plot. Results were different for GWR and LMM. GWR models failed at prediction, but had good results on training plots, especially considering the reduction of autocorrelation of model residuals. LMM provided the best results for both training and validation plots

Cibulka, O. Factors influencing the amount of sales in accommodation and catering facilities in the Czech Republic. Diploma thesis. Brno: Mendel University, 2019. The thesis deals with the quantification of factors affecting the amount of sales in the area of accommodation, catering and hospitality in the Czech Republic. In addition to assessing the significance of these factors, the development of sales in the observed area is examined. To do this, a model is drawn up that describes the behaviour of sales of the given facilities, and on the basis of which the prediction of values for the next 5 quarters is then made. Subsequently, the impact of the financial crisis on the size of sales in the observed area is clarified. Finally, according to the findings, appropriate recommendations for improving the quality of provided services in the observed area are created.

The diploma thesis focuses on the application of Support vector machines (SVM) in the area of bankruptcy prediction. Theoretical research deals with the overview of the passenger transport industry in the EU for each mode of transport individually. Potential causes of bankruptcy in the researched industry are presented based on real examples. Empirical analysis examines the accuracy of SVM classifier with different types of kernels and compares its prediction force with the logistic regression model. In the end, obtained results are summarized, commented on in economic terms and discussed with selected studies.

This thesis deals with evaluation of financial and economical situation in furniture company NADOP-výroba nábytku a.s., for the period 2011-2015. Goal of the work is an elaboration of financial analysis of company's current condition using methods of horizontal and vertical analysis, differential indicators and ratio analysis. Theoretical and methodological basis and other aspects are described in the theoretical part. Practical part contains developed financial analysis based on which are evaluated the current situation and further developement of the company, and a prediction of financial distress. Part of the thesis is also proposal of tools and procedures for improving financial management based on the found results.

The main subject of this doctoral thesis is the influence of the geometrical accuracy of large CNC machine tools on desired features of produced work pieces. Doe to globalized market environment and competition producers of machine tools have changed their strategy for delivery of their products to customers. The main issue is not only to deliver a machine tool as such; supporting instructions related to the technology of the cutting process on the machine tool are of great importance. When taking delivery, the customer can see a new machine tool that will produce by him specified work piece with a desired accuracy. In the proposed thesis, a development of a novel methodology of measuring vertical lathes for prediction of chosen geometrical parameters of work pieces is introduced. The main goal of this work has been to determine the influence of the geometrical accuracy of selected design groups of a vertical lath on the future geometric accuracy of the work piece. The proposed methodology has been developed and verified on a selected vertical lath SKIQ30 produced by TOSHULIN, a.s. For identification of chosen parameters of the vertical lath a measuring system using latest measuring technologies has been applied. The basic tool for measured data processing has been a set of statistic methods for prediction of behaviour of measured design groups of the machine. The foundation for statistical processing has been calculation of geometrical deviations obtained from algorithms designed for proposed measurement methodology. The proposed measurement methodology for vertical lathes has been divided into two parts. In the first part, the methodology of measurement and evaluation of linear axes is solved, where a measuring system Laser Track has been used. The employment of the system Laser Track turned out to be very suitable. Conclusions related to the accuracy of the measuring device have been drawn in the thesis. The second part of the proposed methodology is represented by observation and description of the rotating disk, where non-contact position transducers have been used. In the course of the doctoral dissertation it has been observed that the studied (with respect to the geometry) behaviour of the machine is significantly affected by the cutting conditions. To these belong the loading of the rotating disc by the mass of the work piece, angular velocity of the rotating disc and the operating time of the machine. Based on these observations it can be stated that for prediction of work piece features it is essential to know the behaviour of the machine tool in the whole range of the operating speeds and loading of the rotating disc. A part of the proposed methodology for measuring vertical lathes seems to be very suitable for a design of a diagnostic system that could be applied on large rotating disc. Furthermore, it is recommended to extend the doctoral thesis in order to develop a unit for compensation of geometrical errors on rotating discs of vertical lathes.

This thesis deals with comparing two approaches to modelling and predicting time series: a traditional one (the ARIMAX model) and a modern one (gradiently boosted decision trees within the framework of the XGBoost library). In the first part of the thesis we introduce the theoretical framework of supervised learning, the ARIMAX model and gradient boosting in the context of decision trees. In the second part we fit the ARIMAX and XGBoost models which both predict a specific time series, the daily volume of the S&P 500 index, which is a crucial task in many branches. After that we compare the results of the two approaches, we describe the advantages of the XGBoost model, which presumably lead to its better results in this specific simulation study and we show the importance of hyperparameter optimization. Afterwards, we compare the practicality of the methods, especially in regards to their computational demands. In the last part of the thesis, a hybrid model theory is derived and algorithms to get the optimal hybrid model are proposed. These algorithms are then used for the mentioned prediction problem. The optimal hybrid model combines ARIMAX and XGBoost models and performs better than each of the individual models on its own. 1

The gross domestic product (GDP) is an essential measure of the state of economic activity and serves as a crucial tool for policymakers, investors, or businesses. However, the official GDP estimate in the Czech Republic is only available with a lag of approximately 60 days, and the Czech National Bank (CNB) announces its GDP forecast once in each quarter. This thesis focuses on predicting GDP growth in the current quarter, referred to as nowcasting. I employ several methods to nowcast the real GDP growth in the Czech Republic in a pseudo-real-time setting and compare their performance. Additionally, I investigate the possibility of creating an ensemble model by using a weighted average of several nowcasting models. The results suggest that the Dynamic Factor Model (DFM) performs best in the GDP nowcasting task, and its predictive accuracy is comparable with the official CNB nowcast. Furthermore, the model averaging process yields accuracy close to the best individual model while addressing model uncertainty. The GDP nowcast of the DFM will be made available to the public in real-time on a website and updated with a daily frequency.

Title: About identification of motor predispositions in triathlon Aims: The aim of our study was to find and verify appropriate indicators predicting the on-coming performance in a short triathlon and determine their level for the junior category. Methods: In our study, we combined quantitative and qualitative approaches with a predominance of quantitative methods. In the first phase of the research, we used quantitative methods, confirmatory factor analysis for verification of models of predispositions and elaboration of performance standards for individual tests by means of T-points. In the second part of the research, we used the qualitative method (casuistic studies) to validate the test battery and its application in practice. Results: We have compiled a structural model of motor predispositions for short triathlon, which explained 91% of all cross-correlations of seventeen used indicators. Predispositions for triathlon were categorized into five separate groups; for swimming, cycling and running (i.e. according to individual disciplines), and functional and psychological predispositions. Finding a lower limit boundary of performance in the completed tests is considered to be the most important issue for the assessment of future performance in triathlon. As a lower limit we have set the zone...

The diploma thesis deals with mathematical modeling of the resource allocation problem in an energy system with respect to technical parameters of the used resources. The model includes random input variables affecting the amount of demand and constraints related to associated risks. The thesis addresses control of the operation of various types of boilers and also extends the system with a heat storage tank examining its impact on the behavior of the system and achieved results. The optimization model is based on a multi-period two-stage scenario model of stochastic programming and works with simulated data, which combines real data, statistically determined estimates, and the use of logistic regression. The implementation utilizes GAMS software. When comparing the achieved results with the current state, it was found that the heat storage tank has a positive effect on the function of the system as it allows for extended usage of the cheaper unregulated sources by storing surplus heat, and thus helps to reduce the overall costs of the system.