Neural Networks Meta-Learning for Foreign Exchange Rate Ensemble Forecasting

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 107)

Artificial neural network (ANN), first introduced by McCulloch and Pitts (1943), is a system derived through neuropsychology models (Hertz, 1989). It attempts to emulate the biological system of the human brain in learning and identifying patterns. Moreover, ANNs can more aptly recognize poorly defined patterns. Instead of extracting explicit rules from sample data, the ANN employs a learning algorithm to automatically: (a) extract the functional relationship between input and output, which is embedded in a set of historical data (called training exemplars or learning samples), and (b) encode it in connection weights. Training exemplars that are readily available allow neural networks to capture a large volume of information in a rather short period of time and to continuously learn throughout their lifespan. Furthermore, neural networks have the ability to not only deal with noisy, incomplete, or previously unseen input patterns, but to also generate a reasonable response (Tsaih et al., 1998). However, ANN is far from being optimal learner. For example, the existing studies (e.g., Breiman (1999)) have found that the ways neural networks have of getting to the global minima vary and some networks just settle into local minima instead of global minima through the analysis of error distributions. In this case, it is hard to justify which neural network’s error reaches the global minima if the error rate is not zero. Thus, it is not wise choice that only selecting a single neural network model with the best generalization from a limited number of neural networks if the error is larger than zero.

The main motivation of this study is to take full advantage of the inherent learning capability of meta-learning technique and to design a powerful neural network ensemble learning model. The rest of this chapter is organized as follows. Section 11.2 gives a brief introduction of neural network learning paradigm. In Section 11.3, a neural-network-based meta-learning process is provided in detail. To verify the effectiveness of the proposed meta-learning technique, an exchange rate prediction experiment is performed in Section 11.4. Finally, Section 11.5 concludes this chapter.


Root Mean Square Error Neural Network Model Ensemble Model Foreign Exchange Rate Training Subset 
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© Springer Science+Business Media, LLC 2007

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