This thesis is concerned with future trend prediction on capital markets on the basis of neural networks. Usage of convolutional and recurrent neural networks, Elliott wave theory and scalograms for capital market's future trend prediction is discussed. The aim of this thesis is to propose a novel approach to future trend prediction based on Elliott's wave theory. The proposed approach will be based on the principle of classification of chosen patterns from Elliott's theory by the way of convolutional neural network. To this end scalograms of the chosen Elliott patterns will be created through application of continuous wavelet transform on parts of historical time series of price for chosen stocks.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:399447 |
| Date | January 2019 |
| Creators | Volný, Miloš |
| Contributors | Budík, Jan, Dostál, Petr |
| Publisher | Vysoké učení technické v Brně. Fakulta podnikatelská |
| Source Sets | Czech ETDs |
| Language | Slovak |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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