Abstract
Neural Networks (NN), with their distributed parallel processing power, can be used as a tool to forecast stock exchange(SE) events, if these are seen as Time-Series(TS). In this paper we present a system for SE events prediction, based on an energy function that we deduce from the Lyapunov (L- also called infinite) norm. We focus on the mathematical deductions of the energy function and on the error minimization procedures. We present some comparative results of our method, the classical backpropagation method(BP), and the random walk generator.
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© 1999 Springer-Verlag London Limited
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Cristea, A.I., Okamoto, T. (1999). A L-based Energy Function for SE Prediction. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN VIETRI-98. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0811-5_33
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DOI: https://doi.org/10.1007/978-1-4471-0811-5_33
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1208-2
Online ISBN: 978-1-4471-0811-5
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