Unstable weights in the combination of forecasts
Management Science
Multilayer feedforward networks are universal approximators
Neural Networks
Machine Learning
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A perspective view and survey of meta-learning
Artificial Intelligence Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
International Journal of Intelligent Systems
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
Journal of Management Information Systems
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks
Multistage neural network metalearning with application to foreign exchange rates forecasting
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Parallel consensual neural networks
IEEE Transactions on Neural Networks
Constructing prediction intervals for neural network metamodels of complex systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Developing optimal neural network metamodels based on prediction intervals
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Expert Systems with Applications: An International Journal
Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait
Expert Systems with Applications: An International Journal
A hybrid modeling approach for forecasting the volatility of S&P 500 index return
Expert Systems with Applications: An International Journal
A D-GMDH model for time series forecasting
Expert Systems with Applications: An International Journal
New robust forecasting models for exchange rates prediction
Expert Systems with Applications: An International Journal
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In financial time series forecasting, the problem that we often encounter is how to increase the prediction accuracy as possible using the financial data with noise. In this study, we discuss the use of supervised neural networks as a meta-learning technique to design a financial time series forecasting system to solve this problem. In this system, some data sampling techniques are first used to generate different training subsets from the original datasets. In terms of these different training subsets, different neural networks with different initial conditions or training algorithms are then trained to formulate different prediction models, i.e., base models. Subsequently, to improve the efficiency of predictions of metamodeling, the principal component analysis (PCA) technique is used as a pruning tool to generate an optimal set of base models. Finally, a neural-network-based nonlinear metamodel can be produced by learning from the selected base models, so as to improve the prediction accuracy. For illustration and verification purposes, the proposed metamodel is conducted on four typical financial time series. Empirical results obtained reveal that the proposed neural-network-based nonlinear metamodeling technique is a very promising approach to financial time series forecasting.