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Condition monitoring of engine block machining - Plasma
Váško, Ondřej ; Krejčí, Jakub (referee) ; Pikula, Stanislav (advisor)
The aim of this thesis is to design and implement two methods of predictive analysis for company Škoda Auto a.s. In the first part I have conducted a literature search on methods of predictive diagnostics. In the next part, with help from thesis consultant in Škoda Auto a.s., the analysis of the assembly line and data blocks from machinery and measuring has been made. Then I designed and programmed data generator based on real data. I created two methods of predictive diagnostics, capable of analyzing input data and deciding about their condition. In the end I tested these two methods and evaluated accuracy of their prediction. Main output of my thesis is two methods of predictive diagnostics, feasible in real world.
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Echo state neural network for stock market prediction
Pospíchal, Ondřej ; Mašek, Jan (referee) ; Burget, Radim (advisor)
This thesis deals with an echo state network and with acceleration of its learning by implementing the echo state network on a graphics processor. The theoretical part consists of the description of neural networks and some selected types of neural networks, on which is based the echo state network. After that, there are some other algorithms described used for time series analysis and last but not least, the tools that were used in the practical part of the thesis were briefly described. The practical part describes the creation of the accelerated version of the echo state network. After that, there is described the creation of input data sets of real financial indexes, on which the echo state network and the other algorithmns were then tested. By analyzing this accelerated version it was found that its learning speed did not reach the theoretical expectations. The accelerated version works slower, but with greater precision. By analyzing the results of the measurement of the other algorithmns it was found that the highest precision is achieved by solutions based on the neural network principle.
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Assessing Economic Situation of a Company and Proposals for Its Improvement
Vitula, Miroslav ; Michalíková, Eva (referee) ; Doubravský, Karel (advisor)
This Master’s Thesis deals with the financial analysis and assessment of the economic situation of Pears Health Cyber s. r. o. with proposals for its improvement. The theoretical part of the thesis is devoted to economic and statistical theory. It describes financial ratios, financial analysis tools, time series, regression and correlation analysis. The analytical part applies selected statistical and financial methods to evaluate the financial situation of the company and evaluates predictions for future years based on the calculated regression models. On the basis of the financial and statistical analysis, suggestions for improving the company's financial situation are developed.
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Condition monitoring of engine block machining - Plasma
Váško, Ondřej ; Krejčí, Jakub (referee) ; Pikula, Stanislav (advisor)
The aim of this thesis is to design and implement two methods of predictive analysis for company Škoda Auto a.s. In the first part I have conducted a literature search on methods of predictive diagnostics. In the next part, with help from thesis consultant in Škoda Auto a.s., the analysis of the assembly line and data blocks from machinery and measuring has been made. Then I designed and programmed data generator based on real data. I created two methods of predictive diagnostics, capable of analyzing input data and deciding about their condition. In the end I tested these two methods and evaluated accuracy of their prediction. Main output of my thesis is two methods of predictive diagnostics, feasible in real world.
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Echo state neural network for stock market prediction
Pospíchal, Ondřej ; Mašek, Jan (referee) ; Burget, Radim (advisor)
This thesis deals with an echo state network and with acceleration of its learning by implementing the echo state network on a graphics processor. The theoretical part consists of the description of neural networks and some selected types of neural networks, on which is based the echo state network. After that, there are some other algorithms described used for time series analysis and last but not least, the tools that were used in the practical part of the thesis were briefly described. The practical part describes the creation of the accelerated version of the echo state network. After that, there is described the creation of input data sets of real financial indexes, on which the echo state network and the other algorithmns were then tested. By analyzing this accelerated version it was found that its learning speed did not reach the theoretical expectations. The accelerated version works slower, but with greater precision. By analyzing the results of the measurement of the other algorithmns it was found that the highest precision is achieved by solutions based on the neural network principle.
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Predictive Analytics - Process and Development of Predictive Models
Praus, Ondřej ; Pour, Jan (advisor) ; Mrázek, Luboš (referee)
This master's degree thesis focuses on predictive analytics. This type of analysis uses historical data and predictive models to predict future phenomenon. The main goal of this thesis is to describe predictive analytics and its process from theoretical as well as practical point of view. Secondary goal is to implement project of predictive analytics in an important insurance company operating in the Czech market and to improve the current state of detection of fraudulent insurance claims. Thesis is divided into theoretical and practical part. The process of predictive analytics and selected types of predictive models are described in the theoretical part of the thesis. Practical part describes the implementation of predictive analytics in a company. First described are techniques of data organization used in datamart development. Predictive models are then implemented based on the data from the prepared datamart. Thesis includes examples and problems with their solutions. The main contribution of this thesis is the detailed description of the project implementation. The field of the predictive analytics is better understandable thanks to the level of detail. Another contribution of successfully implemented predictive analytics is the improvement of the detection of fraudulent insurance claims.
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