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Travel time prediction
Mudroch, Andrej ; Janáková, Ilona (referee) ; Honec, Peter (advisor)
This thesis discusses travel time prediction of vehicles on roads based on the methods of machine learning. It describes theory of travel times and summarizes scientific papers dealing with this topic. Within the thesis, analysis of real travel time data was done and the features to be used in prediction models were engineered. Finally, the complex prediction system was designed and implemented and has been tested in production environment.
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Evaluation of Macroeconomic forecasting accuracy
Polák, Zdeněk ; Skuhrovec, Jiří (advisor) ; Martišková, Monika (referee)
This thesis deals with real GDP growth forecasting. It includes comparison of predictive performance of OECD, IMF, European Commission, and Ministry of Finance of the Czech Republic in period between 2000 and 2010. Forecast errors for Central European countries are analyzed and compared to forecast errors for G7 countries, which has never been done before. Organizations are benchmarked based on summary statistics, comparison with nave forecast, and directional and sign accuracy. Results of the analysis show that forecasts for expansion period are more accurate than forecasts for recession period. Furthermore, hypothesis that forecasts for G7 countries are on average more accurate than forecasts for Central European countries is not confirmed. This is particularly interesting for the Ministry of Finance of the Czech Republic, which did not outperform other organizations in forecasts for the Czech Republic and apparently has no comparative advantage in predicting economic development of the Czech Republic.
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Prognostic and Predictive Factors in Breast Cancer
Šefrhansová, Lucie ; Fínek, Jindřich (advisor) ; Pešek, Miloš (referee) ; Tesařová, Petra (referee) ; Nekulová, Miroslava (referee)
of dissertation thesis Prognostic and Predictive Factors in Breast Cancer The mRNA Expression of Selected Genes in Normal and Tumor Breast Tissue Samples and Theirs Clinical Value in Breast Cancer L.Šefrhansová Background: The aim of this work was to describe and to evaluate possibilities of prognosis and prediction in breast cancer. Within the framework of this study-work we carry out a prospective clinical study. The aim of this prospective study was to detect mRNA MMP-7, p53 and TIMP-1 expression in normal and tumor breast tissue samples and to determine the clinical and prognostic significance of our results. Prognosis and prediction: The tumor size, lymph node status, presence of distant metastasis, differentiation of the tumor, perivascular invasion, mitotic activity, expression of ER, PR and HER2 receptors are the basic prognostic factors in breast cancer. Age under/above 35 years was included among independent prognostic breast cancer factors in 2005. It is approved to use uPA/PAI to assess prognosis in node negative breast cancer patients. The hormone receptor status and HER-2 receptor status are the only two predictive markers associated with the target therapy. OncotypeDX analysis could be use to predict the disease recurrence interval of patients with estrogen positive and node negative...
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Range-based volatility estimation and forecasting
Benčík, Daniel ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
In this thesis, we analyze new possibilities in predicting daily ranges, i.e. the differences between daily high and low prices. The main focus of our work lies in investigating how models commonly used for daily ranges modeling can be enhanced to provide better forecasts. In this respect, we explore the added benefit of using more efficient volatility measures as predictors of daily ranges. Volatility measures considered in this work include realized measures of variance (realized range, realized variance) and range-based volatility measures (Parkinson, Garman & Klass, Rogers & Satchell, etc). As a subtask, we empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to simple daily ranges. As another venue of research in this work, we analyze the added benefit of slicing the trading day into different sessions based on trading activity (e.g. Asian, European and American session). In this setting we analyze whether whole-day volatility measures reliably aggregate information coming from all trading sessions. We are led by intuition that different sessions exhibit significantly different characteristics due to different order book thicknesses and trading activity in general. Thus these sessions are expected to provide valuable information concealed in...
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Methods of artificial intelligence and their use in prediction
Šerý, Lubomír ; Omelka, Marek (advisor) ; Krtek, Jiří (referee)
Title: Methods of artificial intelligence and their use in prediction Author: Lubomír Šerý Department: Department of Probability and Mathematical Statistics Supervisor: Ing. Marek Omelka, Ph.D., Department of Probability and Mathe- matical Statistics Abstract: In the presented thesis we study field of artificial intelligence, in par- ticular we study part dedicated to artificial neural networks. At the beginning, concept of artificial neural networks is introduced and compared to it's biological base. Afterwards, we also compare neural networks to some generalized linear models. One of the main problems of neural networks is their learning. Therefore biggest part of this work is dedicated to learning algorithms, especially to pa- rameter estimation and specific computational aspects. In this part we attempt to bring in an overview of internal structure of neural network and to propose enhancement of learning algorithm. There are lots of techniques for enhancing and enriching basic model of neural networks. Some of these improvements are, together with genetic algorithms, introduced at the end of this work. At the very end of this work simulations are presented, where we attempt to verify some of the introduced theoretical assumptions and conclusions. Main simulation is an application of concept of neural...
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Technical analysis of stock trends using artificial neural networks
John, Pavel ; Petříčková, Zuzana (advisor) ; Pilát, Martin (referee)
Although the discipline has not received the same level of acceptance in the past, the technical analysis has been part of financial practice for centuries. One of the big issues was the absence of widely respected fully rational background that is necessary for the modern science. The presence of geometrical shapes recognized by a human eye in historical data charts remained as one of the most important tools till the last decades. Nowadays, it is possible to find commercial trading software which employs neural networks. However, a freely accessible tool is difficult to obtain. The aim of this work was to investigate the usability of applications of neural networks on the technical analysis and to develop a software tool that would implement the knowledge acquired. An application was created and a new promising trading strategy proposed along with experimental data. The advantages of the program presented include the ease of extensibility and a high variability in trading strategies setting.
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Technical analysis of financial time series
Faltýnková, Anežka ; Petrásek, Jakub (advisor) ; Hurt, Jan (referee)
The thesis studies the problem of inefficiencies in the finan- cial markets. The first section describes the fundamental concepts, such as the efficient market hypothesis and futures contracts. The necessary mathematics is summarized in the second part, which deals with the link between the futures price and the martingale. The nonlinear regression is introduced and the greatest emphasis is placed on the description of the functional linear model with a scalar response. The main part focuses on the application of this theory. Two models are proposed for predicting prices based on their historical changes. The first model is nonlinear and is based on the assumption that the impact of the price change on the prediction process diminishes exponentially with time. The second one is linear and directly estimates the effect of particular changes. Both models are compared in terms of their ability to predict inefficiencies, calculation costs and stability. 1
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Algorithmic Trading Using Twitter Data
Kříž, Jakub ; Plchot, Oldřich (referee) ; Szőke, Igor (advisor)
This master's thesis describes creation of prediction system. This system predicts future market development based on stock exchange data and twitter messages analysis. Tweets from two different sources are analysed by mood dictionaries or via recurrent neural networks. This analysis results and technical analysis of stock exchange data results are used in multilayer neural network for prediction. A business strategy is created and tested based on results of this prediction. Design and implementation of prediction system is described in this thesis. This system achieved revenue increase more than 25 % of some business strategies by tweets analysis. However this improvement applies for certain data and timeframe.
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Application of econometric and simulation models in organizing a running race
Mihál, Filip ; Kuncová, Martina (advisor) ; Novotný, Jakub (referee)
This diploma thesis deals with application of econometric and simulation models in organizing relay running race od Tatier k Dunaju. In theoretical part the reader is introduced to basic theory regarding econometric and simulation models. Detailed introduction to od Tatier k Dunaju running race follows afterwards. There are used multiple regression models in this thesis which are used to estimate runner s time in his sector based on 10 km run reported time and track profile. Results are compared to those provided by organizer himself and between each other. Simulation models are used to analyze sufficiency of parking lots capacity, which are used by team cars while traveling between sectors. These models are compared to reality and afterwards suggestions to solve recognized issues are discussed.
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