Boosting and Bagging of Neural Networks with Applications to Financial Time Series

Boosting and bagging are two techniques for improving the performance of learning algorithms. Both techniques have been successfully used in machine learning to improve the performance of classification algorithms such as decision trees, neural networks. In this paper, we focus on the use of feedforward back propagation neural networks for time series classification problems. We apply boosting and bagging with neural networks as base classifiers, as well as support vector machines and logistic regression models, to binary prediction problems with financial time series data. For boosting, we use a modified boosting algorithm that does not require a weak learner as the base classifier. A comparison of our results suggest that our boosting and bagging techniques greatly outperform support vector machines and logistic regression models for this problem. The results also show that our