基于无迹卡尔曼滤波的LSSVR在线多步时间序列预测

准确宽范围多步预测在时间序列预测应用中带来了巨大挑战.提出了一种基于最小二乘支持向量回归(LSSVR)和无迹卡尔曼滤波(UKF)的在线多步预测方法,利用时间滑动窗口减小算法的计算负荷,UKF方法实现LSSVR模型参数更新以提高预测精度.当预测范围达到预定步长p时,由核宽度σ、支持值参数{α_k}_k~L_(=1)以及偏移项b所构成的模型参数通过新的测量值和UKF进行在线更新.提出的方法不仅以较少的训练数据建立在线预测模型(所需训练数据集大小为相空间维数与滑动窗口长度之和),且多步预测值的精度相比于传统方法得到进一步提高.最后,通过几个实验研究论证了提出方法的有效性和优越性.

Multi-step-ahead Time Series Prediction based on LSSVR using UKF with Sliding-Windows

Accurate multi-step-ahead prediction over long future horizons posts great challenges for the application of time series prediction.A novel online multi-step-ahead prediction method based on least squares support vector regression(LSSVR)is proposed.Taking the superiorities of using sliding-windows to reduce largely computation burden and implementing LSSVR model updating by Unscented Kalman Filter(UKF),the proposed method not only can construct online predicted model in much fewer training data(such as the size of original training data set required is only the sum of embedding dimension corresponding to phase-space-reconstruction and the length of sliding-windows),but also has the better accuracy over multi-step-ahead prediction.When the prediction horizon reached the predefined step p in the process of predicting,model parameters consisted of kernel widthσ,support values{α_k}_k~L_(=1)and bias term b are updated by new arrived measurements and UKF.Finally,several simulations are provided to show the validity and applicability of the proposed method.