基于残差预测修正的局部在线时间序列预测方法

 对于复杂的非线性和非平稳时间序列预测,基本的支持向量回归(Support Vecotr Regression,SVR)在线算法无法有效兼顾执行效率和预测精度.本文首先采用局部SVR进行时间序列建模预测,同步计算在线更新序列数据预测的残差,并采用Online SVR对残差序列进行混沌时间序列预测,将预测残差值实时补偿到局部SVR模型预测输出.实验结果表明,新方法在执行效率和预测精度方面较单一Online SVR均显著提高.

Local Online Time Series Prediction Based on the Residual Compensation with Online SVR

For complicated nonlinear and nonstationary time series prediction,the precision will be decreased if a faster processing speed is reached in online SVR.A new time series prediction method is proposed.Local SVR is firstly adopted to make prediction of the time series.Then the forecast residual with the real data stream is calculated.Finally,the residual is estimated with online SVR algorithm to compensate the predicted value with the local SVR.Experimental results showed that the proposed method outperforme...