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Time Series Forecasting Using Machine Learning
Elrefaei, Islam ; Galáž, Zoltán (referee) ; Hošek, Jiří (advisor)
The aim of this thesis is to explore the application of various artificial intelligence (AI) techniques for the prediction of time series data, which is prevalent in fields such as finance, economics, and engineering. Accurate time series prediction is essential for effective decision-making and planning. This thesis reviews several traditional and state-of-the-art AI techniques used for time series prediction, including linear regression, ARIMA, support vector regression, random forests, and deep learning. These techniques are applied to different time series datasets, encompassing both univariate and multivariate data. The performance of the predictive models is evaluated using various scalar metrics. The performance of the models was different depending on the type of the dataset. Additionally, this thesis includes the development of a user interface application that allows users to change parameters and forecast new results based on their entries. Furthermore, the thesis discusses the challenges and limitations of using AI techniques for time series prediction and provides suggestions for future research directions.
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Essays on Data-driven, Non-parametric Modelling of Time-series
Hanus, Luboš ; Vácha, Lukáš (advisor) ; Witzany, Jiří (referee) ; Ellington, Michael (referee) ; Trimborn, Simon (referee)
This thesis consists of four contributions to the literature on data-driven and non-parametric modelling of time series. In the first paper, we study the synchronisation of business cycles and propose a multivariate co-movement measure based on time-frequency cohesion. We suggest that economic inte- gration may lead to increased co-movement of business cycles, which may reflect the benefits of convergence and coordination of economic policies. The second paper presents a new methodology for identifying persistence in macroeconomic variables. Using time-varying frequency response func- tions, we identify heterogeneous persistence effects in US macroeconomic variables. The third and fourth papers propose data-driven techniques for probabilistic forecasting of time series using deep learning. We introduce a multi-output neural network that selects the most appropriate distribution for the data. The distributional neural network is valuable for modelling data with non-linear, non-Gaussian and asymmetric structures. The third paper demonstrates the usefulness of the method by estimating information-rich macroeconomic fan charts and distributional forecasts of asset returns. In the last paper, we present the distributional neural network to obtain the proba- bility distribution of electricity price...
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Knowledge Discovery from Time Series
Krutý, Peter ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis is focused on the field of knowledge discovery from data, specifically from time series. Main objective is to research Python programming language support in this area and then design and implement an application that will allow to demonstrate and compare selected methods. Methods are demonstrated in experiments using appropriate data set. The output of the thesis is a comparison of methods for specific tasks and the application implementing selected methods.
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Analyze and economic time series forecasting by using selected statistical methods
Skopal, Martin ; Charvát, Pavel (referee) ; Mauder, Tomáš (advisor)
V této diplomové práci se zaměřujeme na vytvoření plně automatizovaného algoritmu pro předpovědi finančních řad, který se snaží využít kombinační proceduru na dvou úrovních mezi dvěma rodinami předpovědních modelů, Box-Jenkins a Exponenciální stavové modely, které jsou schopny modelovat jak homoskedastické tak heteroskedastické časové řady. Pro tento účel jsme navrhli selekční proceduru v prostředí MATLAB pro modely ARIMA. Výsledný kombinovaný model je pak aplikován několik finančních časových řad a jeho výkonost je diskutována.
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Analysis of a Selected Company Using Statistical Methods
Poláček, Lukáš ; Veverková, Jana (referee) ; Doubravský, Karel (advisor)
The master’s thesis deals with the aggregate reviews of the economic situation of one joint-stock company, primarily using statistical methods. The goal will be to analyse the data by these means, to compare them, to make the conclusions and suggestions for improvement. From the knowledge of historical data and forecasting preconditions for the future the company will gain clearer image about its development and future directions.
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The Use of Statistical Methods for Data Processing
Zbranková, Kateřina ; Zeman, Karel (referee) ; Novotná, Veronika (advisor)
This diploma thesis deals with the evaluation of the economic situation of ZLKL, s. r. o. using statistical methods. Primarily the thesis proceeds from financial records of the company which are put through the financial analysis. On the basis of its results the statistical analysis of chosen indicators is then accomplished. Using statistical methods it tries to analyse the development of each indicator, its trend, and to predict its future development. In the last part of the thesis, there is the evaluation of each indicator, and the formulation of suggestions and recommendations by whose implementation the company should achieve the bigger financial stability and the long-term stable management.
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