Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Support Vector Mixture for Classification and Regression Problems
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Dynamic support vector machines for non-stationary time series forecasting
Intelligent Data Analysis
A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A dynamic holding strategy in public transit systems with real-time information
Applied Intelligence
The evaluation of consumer loans using support vector machines
Expert Systems with Applications: An International Journal
Time series prediction using support vector machines: a survey
IEEE Computational Intelligence Magazine
Expert Systems with Applications: An International Journal
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A two-stage neural network architecture constructed by combining Support Vector Machines (SVMs) with self-organizing feature map (SOM) is proposed for financial time series forecasting. In the first stage, SOM is used as a clustering algorithm to partition the whole input space into several disjoint regions. A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVMs, also called SVM experts, that best fit each partitioned region are constructed by finding the most appropriate kernel function and the optimal learning parameters of SVMs. The Santa Fe exchange rate and five real futures contracts are used in the experiment. It is shown that the proposed method achieves both significantly higher prediction performance and faster convergence speed in comparison with a single SVM model.