Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Multi-dimensional Function Approximation and Regression Estimation
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Methodology for long-term prediction of time series
Neurocomputing
Incorporating prior knowledge in support vector regression
Machine Learning
Long-term prediction of time series by combining direct and MIMO strategies
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A soft computing system for day-ahead electricity price forecasting
Applied Soft Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Expert Systems with Applications: An International Journal
SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems
IEEE Transactions on Signal Processing
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Optimization Methodology for Neural Network Weights and Architectures
IEEE Transactions on Neural Networks
Quarterly Time-Series Forecasting With Neural Networks
IEEE Transactions on Neural Networks
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Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterated strategy or direct strategy. This study proposes a novel multiple-step-ahead time series prediction approach which employs multiple-output support vector regression (M-SVR) with multiple-input multiple-output (MIMO) prediction strategy. In addition, the rank of three leading prediction strategies with SVR is comparatively examined, providing practical implications on the selection of the prediction strategy for multi-step-ahead forecasting while taking SVR as modeling technique. The proposed approach is validated with the simulated and real datasets. The quantitative and comprehensive assessments are performed on the basis of the prediction accuracy and computational cost. The results indicate that (1) the M-SVR using MIMO strategy achieves the best accurate forecasts with accredited computational load, (2) the standard SVR using direct strategy achieves the second best accurate forecasts, but with the most expensive computational cost, and (3) the standard SVR using iterated strategy is the worst in terms of prediction accuracy, but with the least computational cost.