Bayesian neural networks for nonlinear time series forecasting
Statistics and Computing
Neural networks for event extraction from time series: a back propagation algorithm approach
Future Generation Computer Systems
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
Neural networks for event extraction from time series: a back propagation algorithm approach
Future Generation Computer Systems
Avoiding local minima in feedforward neural networks by simultaneous learning
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
A sparse kernel algorithm for online time series data prediction
Expert Systems with Applications: An International Journal
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Interest in neural networks has expanded rapidly in recent years. Selecting the best structure for a given task, however, remains a critical issue in neural-network design. Although the performance of a network clearly depends on its structure, the procedure for selecting the optimal structure has not been thoroughly investigated, it is well known that the number of hidden units must be sufficient to discriminate each observation correctly. A large number of hidden units requires extensive computational time for training and often times prediction results may not be as accurate as expected. This study attempts to apply the principal component analysis (PCA) to determine the structure of a multilayered neural network for time series forecasting problems. The main focus is to determine the number of hidden units for a multilayered feedforward network. One empirical experiment with sunspot data is used to demonstrate the usefulness of the proposed approach