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Artificial neural networks for macroeconomic data analysis
Padrón Peňa, Ildefonso ; Mrázová, Iveta (advisor) ; Kuboň, David (referee)
The analysis and prediction of macroeconomic time-series is a factor of great interest to national policymakers. However, economic analysis and forecast- ing are not simple tasks due to the lack of a precise model for the economy and the influence of external factors, such as weather changes or political decisions. Our research is focused on Spanish speaking countries. In this thesis, we study dif- ferent types of neural networks and their applicability for various analysis tasks, including GDP prediction as well as assessing major trends in the development of the countries. The studied models include multilayered neural networks, recur- sive neural networks, and Kohonen maps. Historical macroeconomic data across 17 Spanish speaking countries, together with France and Germany, over the time period of 1980-2015 is analyzed. This work then compares the performances of various algorithms for training neural networks, and demonstrates the revealed changes in the state of the countries' economies. Further, we provide possible reasons that explain the found trends in the data.
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Web Application for Making Predictions of a Call Centre
Mička, David ; Hynek, Jiří (referee) ; Bartík, Vladimír (advisor)
The goal of this thesis is to create a web application for creation of call centre predictions. The app should be able to replace current solutions that are in use in the daily operation of Kiwi.com s.r.o. The app should be more intuitive and easier to use and maintain than Verint or the spreadsheet solution of doing predictions. It should also have enough options for creation of tactical forecasts that allow the company to react on upcoming situations and should help set realistic expectations for the management of our customer centre.
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Prediction of Protein Solubility
Marušiak, Martin ; Martínek, Tomáš (referee) ; Hon, Jiří (advisor)
Protein solubility is closely related to the usability of proteins in industrial use and research. The successful prediction of solubility would therefore lead to a significant saving of financial resources. This work presents new solubility predictor Solpex based on machine learning that achieved better performance on independent test set than any comparable solubility prediction tool. The predictor implementation was preceded by a study of the biological nature of solubility, evaluation of existing solubility prediction approaches, datasets building, many experiments with novel features and selection of the best features for the predictor. As the most important step in machine learning is the datasets building, this work mainly benefits from own rigorous processing of the main source of solubility data - the TargetTrack database.
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Machine Learning Optimization of KPI Prediction
Haris, Daniel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.
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Machine learning with applications to finance
Mešša, Samuel ; Hurt, Jan (advisor) ; Večeř, Jan (referee)
The impact of data driven, machine learning technologies across a wide variety of fields is undeniable. The financial industry, which relies heavily on predictive modeling being no exception. In this work we summarize two widely used machine learning models: support vector machines and neural networks, discuss their limitations and compare their performance to a more traditionally used method, namely logistic regression. Evaluation was done on two real world datasets, which were used to predict default of loan applicants and credit card holders formulated as a binary classification task. Neural networks and support vector machines either outperformed or showed comparable results to logistic regression with performance measured in receiver operator characteristic area under curve. In the second task neural networks outperformed both other models by a significant margin.
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The Use of Artificial Intelligence for Decision Making
Nezbedová, Katarína ; Pekárek, Jan (referee) ; Dostál, Petr (advisor)
This bachelor thesis deals with the Tamari attractor problem and its application for forming a prediction model. The core of the work is to create a simulation program in the MATLAB development environment and to use it to create and compare several case studies of a predictive model based on different parameters. This model is graphically illustrated and supplemented by economic interpretation.
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Child with risk for learning disabilities
WALDOVÁ, Petra
This diploma thesis deals with the possibilities of prediction of specific learning disabilities among children of preschool age. The thesis is divided into two consequent parts a theoretical part and a practical part. In the theoretical part I focus mainly on the etiology and the diagnosis of specific learning disabilities. The thesis focuses mainly on given examples of a few predictive batteries from Czech and even foreign authors. The practical part contains results of a research survey where are the data I collected through predictive battery of British authors: Nicolson and Fawcett: The Dyslexia Early Screening Test. The main objective of the diploma thesis was to find out the possibility of using this test by pedagogical workers at pre-schools in order to avoid specific learning disabilities. For the research, the quantitative research method was used.
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