An intelligent forecasting model based on robust wavelet ν-support vector machine
An intelligent forecasting model based on robust wavelet ν-support vector machine
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DOI：
10.1016/j.eswa.2010.09.036
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发表时间：
2011-05
期刊：
影响因子：
--
通讯作者：
Qi Wu;R. Law
中科院分区：
文献类型：
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作者：
Qi Wu;R. Law
针对产品需求序列的小样本性、季节性、非线性、随机性和模糊性等问题，现有的支持向量核函数不能在L2（Rn）空间（二次连续积分空间）中逼近需求时间序列的随机曲线。针对ε-不敏感损失函数在处理混合噪声时的不足，提出了鲁棒损失函数。基于小波理论和改进的支持向量机，提出了一种新的鲁棒小波支持向量机（RW ν-SVM）。设计了粒子群优化算法，在约束允许的范围内选择RW ν-SVM模型的最优参数。在轿车需求预测中的应用结果表明，基于RW ν-SVM模型的预测方法是有效可行的，并与其他方法进行了比较，证明了该方法优于RW ν-SVM等传统方法。
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 L2(Rn) space (quadratic continuous integral space). The robust loss function is also proposed to solve the shortcoming of ε-insensitive loss function during handling hybrid noises. A novel robust wavelet support vector machine (RW ν-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 ν-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 ν-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 ν-SVM and other traditional methods.