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1	Predikce chaotických časových řad / Chaotic time-series prediction Dědič, Martin January 2009 (has links) This thesis focuses on possibility of chaotic (specially economic) time-series prediction. Chaotic time-series are unpredictable in long-term due to their high sensitivity on initial conditions. Nevertheless, their behavior should be more or less predictable in short-term. Goal of this thesis is to show, how much and if any prediction, is possible by non-linear prediction method, and try to reveal or to reject presence of chaotic behavior in them. Work is split into three chapters. Chapter One briefly introduces chosen important concepts and methods from this area. In addition, to describe some prediction methods, there are outlined which indicators and methods are possible to use in order to find possibilities and boundaries of this prediction. Chapter Two is focused on modifications of FracLab software, which is used for create this prediction. Last chapter is experimental. Besides the description of examined time-series and methods, it includes discussion of results.
2	Reservoir Computing: Empirical Investigation into Sensitivity of Configuring Echo StateNetworks for Representative Benchmark Problem Domains Weborg, Brooke Renee January 2021 (has links) No description available. Computer Science Computer Engineering Echo State Network Echo State Networks Recurrent Neural Network Recurrent Neural Networks Reservoir Computing Reservoir Computer Machine Learning Time Series Classification Time Series Prediction Chaotic Time Series Prediction Pattern Generation Echo State Network Architecture Echo State Network Parameters Parameters Architecture

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Predikce chaotických časových řad / Chaotic time-series prediction

Reservoir Computing: Empirical Investigation into Sensitivity of Configuring Echo StateNetworks for Representative Benchmark Problem Domains