基于相空间重构的降雨时间序列降噪及预测研究

Noise Reduction and Prediction of Rainfall Chaotic Time Series Using Reconstruction Technique

为解决降雨混沌时序预测精度较低的问题,基于相空间重构思想,引入改进的局部投影算法进行降雨时序的降噪;采用定性和定量的混沌特性判定方法,指出降雨时序具有明显的混沌特性.并在此基础上构建了基于最小二乘支持向量机的降噪、预测一体化模型并进行了多步预测实验.实验结果表明:降噪前后预测精度相差很大,表明噪声是造成混沌预测方法预测精度较低的主要原因.最后通过与其他预测方法比较,验证了所建立的混沌预测模型预测精度高、误差较小,可用于工程实际.

Not only the noise reduction methods of rainfall chaotic time series with noise and its reconstruction techniques are studied, but also prediction techniques of rainfall chaotic time series are discussed based on chaotic data noise reduction. First, the measurement of rainfall time series are respectively applied for noise reduction using improved local projection method;Then, nonlinear dynamics character of this time series is analyzed, the results of calculating show that there＇s chaotic time series. Based on this, a LLSVM model is built by phase space reconstruction. The influent of rainfall has been disposed can be predicted by this model. The results indicate that a reasonable forecasting result have been achieved through such method.