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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Echo state neural network for stock market prediction
 Pospíchal, Ondřej ; Mašek, Jan (referee) ; Burget, Radim (advisor)
This thesis deals with an echo state network and with acceleration of its learning by implementing the echo state network on a graphics processor. The theoretical part consists of the description of neural networks and some selected types of neural networks, on which is based the echo state network. After that, there are some other algorithms described used for time series analysis and last but not least, the tools that were used in the practical part of the thesis were briefly described. The practical part describes the creation of the accelerated version of the echo state network. After that, there is described the creation of input data sets of real financial indexes, on which the echo state network and the other algorithmns were then tested. By analyzing this accelerated version it was found that its learning speed did not reach the theoretical expectations. The accelerated version works slower, but with greater precision. By analyzing the results of the measurement of the other algorithmns it was found that the highest precision is achieved by solutions based on the neural network principle.
Detailed record
Echo state neural network for stock market prediction
 Pospíchal, Ondřej ; Mašek, Jan (referee) ; Burget, Radim (advisor)
This thesis deals with an echo state network and with acceleration of its learning by implementing the echo state network on a graphics processor. The theoretical part consists of the description of neural networks and some selected types of neural networks, on which is based the echo state network. After that, there are some other algorithms described used for time series analysis and last but not least, the tools that were used in the practical part of the thesis were briefly described. The practical part describes the creation of the accelerated version of the echo state network. After that, there is described the creation of input data sets of real financial indexes, on which the echo state network and the other algorithmns were then tested. By analyzing this accelerated version it was found that its learning speed did not reach the theoretical expectations. The accelerated version works slower, but with greater precision. By analyzing the results of the measurement of the other algorithmns it was found that the highest precision is achieved by solutions based on the neural network principle.
Detailed record

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