基于独立成分分析的含噪声时间序列预测

提出一种基于独立成分分析(ICA)的最小二乘支持向量机(LS-SVM),用于时间序列的多步超前独立预测.用ICA估计预测变量中的独立成分(IC),用不含噪声的IC重新构建时间序列.利用 -最近邻法( -NN)减小训练集的规模,提出一种新的距离函数以降低LS-SVM训练过程的计算复杂度,并用约束条件对预测值进行后处理.使用基于ICA的LS-SVM、普通LS-SVM与反向传播神经网络(BP-ANN),对多个时间序列进行对比预测实验.实验结果表明,基于ICA的LS-SVM的预测性能优于普通LS-SVM和BP-ANN.

Noisy time series prediction using independent component analysis

A least square support vector machine (LS-SVM) based on the independent component analysis(ICA) is proposed
to predict noisy non-stationary time series. ICA is used to estimate the independent components(IC) in the forecasting
variables. After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the
forecasting variables which contain less noise. A k-nearest neighbors(k-NN) approach is used to reduce the size of training
dataset and a new distance function is defined. By selecting similar instances in the training dataset, the complexity of
training a LS-SVM is reduced significantly. A boundary constraint component is developed to limit the predicted values to
a reasonable range. The experimental results show that the proposed approach outperforms both traditional LS-SVM and
BP-artificial neural network(BP-ANN) in the prediction performance of several time series.