Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
The nature of statistical learning theory
The nature of statistical learning theory
A genetic algorithms tutorial tool for numerical function optimisation
Proceedings of the 2nd conference on Integrating technology into computer science education
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Support vector regression with ANOVA decomposition kernels
Advances in kernel methods
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
A tutorial on support vector regression
Statistics and Computing
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
IEEE Transactions on Information Technology in Biomedicine
Support Vector Machine Training for Improved Hidden Markov Modeling
IEEE Transactions on Signal Processing
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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A framework (hereby named GA-SVM) for time series forecasting was formed by integration of the particular power of Genetic Algorithms (GAs) with the modeling power of the Support Vector Machine (SVM). The proposed system has potential to capture the benefits of both fascinating fields into a single framework. GAs offer high capability in choosing inputs that are relevant and necessary in predicting dependent variables. With these selected inputs, SVM becomes more accurate in modeling the estimation problems. Experiments demonstrated that the integrated GA-SVM approach is superior compared to conventional SVM applications.