Abstract
This article aims at knowledge discovery in dynamic data via classification based on neural networks. In our experimental study we have used three different types of neural networks based on Hebb, Adaline and backpropagation training rules. Our goal was to discover important market (Forex) patterns which repeatedly appear in the market history. Developed classifiers based upon neural networks should effectively look for the key characteristics of the patterns in dynamic data. We focus on reliability of recognition made by the described algorithms with optimized training patterns based on the reduction of the calculation costs. To interpret the data from the analysis we created a basic trading system and trade all recommendations provided by the neural network.
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Volna, E., Kotyrba, M., Janosek, M. (2015). Knowledge Discovery in Dynamic Data Using Neural Networks. In: Kim, K. (eds) Information Science and Applications. Lecture Notes in Electrical Engineering, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46578-3_67
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DOI: https://doi.org/10.1007/978-3-662-46578-3_67
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