Abstract
This article deals with a smart time series prediction based on characteristic patterns recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history for the purpose of prediction of subsequent trader’s action. The pattern recognition approach is based on neural networks. We focus on reliability of recognition made by developed algorithms with optimized patterns which also causes the reduction of the calculation costs.
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Volna, E., Janosek, M., Kocian, V., Kotyrba, M. (2013). Smart Time Series Prediction. In: Snášel, V., Abraham, A., Corchado, E. (eds) Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32922-7_22
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DOI: https://doi.org/10.1007/978-3-642-32922-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32921-0
Online ISBN: 978-3-642-32922-7
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