Comparison of new activation functions in neural network for forecasting financial time series |
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Authors: | Gecynalda S da S Gomes Teresa B Ludermir Leyla M M R Lima |
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Affiliation: | (1) Centre of Informatics, Federal University of Pernambuco, Av. Prof. Luis Freire, s/n, Cidade Universitria Recife/PE, Recife, 50732-970, Brazil |
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Abstract: | In artificial neural networks (ANNs), the activation function most used in practice are the logistic sigmoid function and
the hyperbolic tangent function. The activation functions used in ANNs have been said to play an important role in the convergence
of the learning algorithms. In this paper, we evaluate the use of different activation functions and suggest the use of three
new simple functions, complementary log-log, probit and log-log, as activation functions in order to improve the performance
of neural networks. Financial time series were used to evaluate the performance of ANNs models using these new activation
functions and to compare their performance with some activation functions existing in the literature. This evaluation is performed
through two learning algorithms: conjugate gradient backpropagation with Fletcher–Reeves updates and Levenberg–Marquardt. |
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