Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
From regularization operators to support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
ϵ-Descending Support Vector Machines for Financial Time Series Forecasting
Neural Processing Letters
Sparse Online Greedy Support Vector Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
An introduction to variable and feature selection
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Theoretically Optimal Parameter Choices for Support Vector Regression Machines with Noisy Input
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Multiclass Classification with Multi-Prototype Support Vector Machines
The Journal of Machine Learning Research
On Feature Selection through Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Elements of Forecasting
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Optimized Local Kernel Machines for Fast Time Series Forecasting
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Fast Forecasting with Simplified Kernel Regression Machines
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Linear dependency between ε and the input noise in ε-support vector regression
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A model updating strategy for predicting time series with seasonal patterns
Applied Soft Computing
A novel SVR parameter selection base on bi-level programming problem
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A novel estimation of the regularization parameter for Ɛ-SVM
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Prediction of concrete carbonation depth based on support vector regression
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
GMRVVm-SVR model for financial time series forecasting
Expert Systems with Applications: An International Journal
Short term wind speed prediction based on evolutionary support vector regression algorithms
Expert Systems with Applications: An International Journal
Multi-parametric gaussian kernel function optimization for ε-SVMr using a genetic algorithm
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Grey relational grade in local support vector regression for financial time series prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evolutionary support vector machines for time series forecasting
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Expert Systems with Applications: An International Journal
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In this paper, the problem of optimizing SVR automatically for time series forecasting is considered, which involves introducing auto-adaptive parameters C"i and @e"i to depict the non-uniform distribution of the information offered by the training data, developing multiple kernel function K"@s to rescale different attributes of input space, optimizing all the parameters involved simultaneously with genetic algorithm and performing feature selection to reduce the redundant information. Experimental results assess the feasibility of our approach (called Model-optimizing SVR or briefly MO-SVR) and demonstrate that our method is a promising alternative for time series forecasting.