基于小波分解的投影寻踪自回归组合模型及其在年径流预测中的应用

Projection pursuit autoregression model based on wavelet decomposition and its application in annual runoff prediction

为了进一步提高中长期径流预测精度,针对历史径流序列的非线性、随机性的特点,采用对原序列先处理再预测的研究思路,吸取小波分析的分频数据处理功能和投影寻踪自回归的高维数据逼近能力,构建基于小波分解的投影寻踪自回归模型。该模型首先利用小波分解方法将年径流序列分解为一个近似信号和多个细节信号,再对不同信号序列分别建立投影寻踪自回归模型进行预测,最后重构各序列预测结果。以长江宜昌站年径流为实例进行预测,同时探讨小波分解尺度数对组合模型预测结果的影响。结果表明:与投影寻踪自回归模型、BP神经网络模型和ARMA模型相比,新模型提高了预测精度,增强了预测稳定性,并且尺度数对组合模型的预测结果影响不大。

A projection pursuit autoregression model based on wavelet decomposition （PPARWD） has been developed to reveal the characteristics of mid-and-long term runoffs and resolve the problem of low prediction accuracy. This model adopts a new idea, processing then forecasting, and makes use of the multi-resolving power of wavelet analysis and the high-dimensional approaching capacity of projection pursuit autoregression （PPAR）. It decomposes a time series of annual runoff into one approximate signal and several detailed signals by wavelet decomposition, and then uses the PPAR model to predict each of these signal series and reconstructs the final results. This PPARWD model is applied to the annual runoff at the Yichang hydrological station, and compared it with the PPAR model, a BP neural networks model and an autoregressive moving average （ARMA） model. The results show that it has better prediction accuracy and stability and its predictions are insensitive to the decomposed scale coefficients.