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Prediction of Multiple Time Series at Stock Market Trading
Palček, Peter ; Zbořil, František (referee) ; Rozman, Jaroslav (advisor)
The diploma thesis comprises of a general approach used to predict the time series, their categorization, basic characteristics and basic statistical methods for their prediction. Neural networks are also mentioned and their categorization with regards to the suitability for prediction of time series. A program for the prediction of the progress of multiple time series in stock market is designed and implemented, and it's based on a model of flexible neuron tree, whose structure is optimized using immune programming and parameters using a modified version of simulated annealing or particle swarm optimization. Firstly, the program is tested on its ability to predict simple time series and then on its ability to predict multiple time series.
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Phillips curve verification by time series analysis of Czech republic and Germany
Král, Ondřej ; Arltová, Markéta (advisor) ; Blatná, Dagmar (referee)
Government fiscal and monetary policy has long been based on the theory that was neither proven nor refuted since its origination. The original form of the Phillips curve has undergone significant modifications but its relevance remains questionable. This thesis examines the correlation between inflation and unemployment observed in the Czech Republic and Germany over the last twenty years. The validity of the theory is tested by advanced methods of time series analysis in the R environment. All the variables are gradually tested which results in the assessment of the correlation between the time series. The outcome of the testing is presented for both countries and a comparison at international level is drawn. Is is discovered that both of the countries have dependencies in their data. Czech republic has significant dependency in both ways, for Germany is the dependency significantly weaker and only in one way.
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Time series models with exogenous variables and their application to economical data
Vaverová, Jana ; Zichová, Jitka (advisor) ; Cipra, Tomáš (referee)
This thesis deals with analyzing multivariate financial and economical data. The first section describes the theory of multivariate time series and multivariate ARMA models. The second part deals with some models with exogenous variables such as simultaneous equations models and ARMAX model. In the final chapter, the described theory is applied to analyze the reciprocal dependence of time series of inflation rates and dependence of inflation rates on various macroeconomical indicators. The results were obtained by software Mathematica 8, Mathematica 10, EViews and R. Powered by TCPDF (www.tcpdf.org)
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Prediction of Multiple Time Series at Stock Market Trading
Palček, Peter ; Zbořil, František (referee) ; Rozman, Jaroslav (advisor)
The diploma thesis comprises of a general approach used to predict the time series, their categorization, basic characteristics and basic statistical methods for their prediction. Neural networks are also mentioned and their categorization with regards to the suitability for prediction of time series. A program for the prediction of the progress of multiple time series in stock market is designed and implemented, and it's based on a model of flexible neuron tree, whose structure is optimized using immune programming and parameters using a modified version of simulated annealing or particle swarm optimization. Firstly, the program is tested on its ability to predict simple time series and then on its ability to predict multiple time series.
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