Multilayer feedforward networks are universal approximators
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Neural network models for time series forecasts
Management Science
An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
Expert Systems with Applications: An International Journal
Moderating the outputs of support vector machine classifiers
IEEE Transactions on Neural Networks
A new Chance-Variance optimization criterion for portfolio selection in uncertain decision systems
Expert Systems with Applications: An International Journal
A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
Aiming at the problem of small samples, season character, nonlinearity, randomicity and fuzziness in product demand series, the existing support vector kernel does not approach the random curve of the demands time series in the L^2(R^n) space (quadratic continuous integral space). The robust loss function is also proposed to solve the shortcoming of @e-insensitive loss function during handling hybrid noises. A novel robust wavelet support vector machine (RW @n-SVM) is proposed based on wavelet theory and the modified support vector machine. Particle swarm optimization algorithm is designed to select the optimal parameters of RW @n-SVM model in the scope of constraint permission. The results of application in car demand forecasts show that the forecasting approach based on the RW @n-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than RW @n-SVM and other traditional methods.