Abstract
Financial time series has been standard complex problem in the field of forecasting due to its non-linearity and high volatility. Though various neural networks such as back propagation, radial basis, recurrent and evolutionary etc. can be used for time series forecasting, each of them suffer from some flaws. Performances are more varied for different time series with loss of generalization. Each of the method poses some pros and cons for it. In this paper, we use ensembles of neural networks to get better performance for the financial time series forecasting. For neural network ensemble four different modules has been used and results of them are finally integrated using integrator to get the final output. Gating has been used as integration techniques for the ensembles modules. Empirical results obtained from ensemble approach confirm the out performance of forecast results than single module results.
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Tarsauliya, A., Kala, R., Tiwari, R., Shukla, A. (2011). Financial Time Series Forecast Using Neural Network Ensembles. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_57
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DOI: https://doi.org/10.1007/978-3-642-21515-5_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21514-8
Online ISBN: 978-3-642-21515-5
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