Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Neural networks versus time series models for forecasting commodity prices
Neural networks versus time series models for forecasting commodity prices
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Multi-agent System Approach to React to Sudden Environmental Changes
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Accuracy of MLP based data visualization used in oil prices forecasting task
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
The multi-agent system for prediction of financial time series
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
| Hi-index | 0.00 |
In dynamic financial time series prediction, neural network training based on short data sequences results to more accurate predictions as using lengthy historical data. Optimal training set size is determined theoretically and experimentally. To reduce generalization error we: a) perform dimensionality reduction by mapping input data into low dimensional space using the multilayer perceptron, b) train the single layer perceptron classifier with short sequences of low-dimensional input data series, c) each time initialize the perceptron with weight vector obtained after training with previous portion of the data sequence, d) make use of useful preceding historical information accumulated in the financial time series data by the early stopping procedure.