基于小波分解及Arima误差修正的径流预测模型及应用

为改善传统径流预测模型对随机性时间序列的预测效果并不理想的现状,构建基于小波分解及Arima误差修正的径流预测模型。应用小波分解法将径流时间序列进行分解和重构,使非平稳、随机性的径流时间序列平稳化,对数据样本预处理后建立以相关向量机(RVM)为理论基础的径流预测模型,并采用改进粒子群算法进行核函数全局寻优,最后对模型拟合残差进行Arima误差修正。通过实例计算得到传统支持向量机(SVM)模型、RVM模型和径流预测模型的预测值平均误差分别为8.60%,9.02%和3.64%。结果表明:通过小波分解及重构方法对非平稳时间序列的预处理可有效提高预测精度,同时Arima误差修正也有很好的效果,相比于SVM模型、RVM模型,基于小波分解及Arima误差修正的径流预测模型具有更高的预测精度,在实际工程中具有一定的可行性。

Runoff Prediction Model Based on Wavelet Decomposition and Arima Error Correction: Research and Application

To improve the prediction effect of traditional runoff prediction model for stochastic time series, a forecast model of runoff based on wavelet decomposition and Arima error correction is proposed to achieve higher predictionprecision in this paper. The wavelet decomposition method is employed to decompose and reconstruct runoff time series, and smooth the non-stationary and random runoff time series. After data pre-processing, the runoff forecast model is built based on relevance vector machine (RVM), the improved particle swarm optimization (IPSO) algorithm is used for optimization, and finally the fitting errors are corrected by Arima model. Case study demonstrates that the average predictive errors of SVM model, RVM model and the proposed model are 8.60%, 9.02%, and 3.64%, respectively. Results prove that wavelet decomposition and reconstruction of time series could effectively enhance prediction precision; meanwhile, Arima error correction also has sound effect. The proposed model is of higher precision with the standard SVM model and RVM model, and therefore is feasible in engineering practice.