Comparative evaluation of genetic algorithm and backpropagation for training neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Classifier ensembles: Select real-world applications
Information Fusion
Engineering Applications of Artificial Intelligence
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
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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 backpropagation, 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 outperformance of forecast results than single module results.