A four-stage hybrid model for hydrological time series forecasting.
A four-stage hybrid model for hydrological time series forecasting.
复制标题
水文时间序列预测的四阶段混合模型
DOI：
10.1371/journal.pone.0104663
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Wang X
中科院分区：
文献类型：
--
作者：
Di C;Yang X;Wang X
水文时间序列由于其复杂的非线性、非平稳和多尺度特征，一直是一项艰巨的任务。为了解决这一困难，提高预测精度，提出了一种基于“去噪、分解、集合”原理的水文时间序列四阶段混合预测模型。该模型分为去噪、分解、分量预测和集成四个阶段。在降噪阶段，利用经验模态分解（EMD）方法对水文时间序列进行降噪。然后，采用一种改进的EMD方法，即集成经验模态分解（EEMD），将降噪后的序列分解为多个本征模态函数（IMF）分量和一个残差分量。接下来，采用径向基函数神经网络（RBFNN）对分解阶段得到的所有分量的趋势进行预测。在最后的集合预测阶段，使用线性神经网络（LNN）模型，将第三阶段获得的所有IMF和残差分量的预测结果结合起来，生成最终的预测结果。为了说明和验证，用6个不同特征的水文实例验证了模型的有效性。该混合模型的性能优于传统的单一模型、不去噪或不分解的混合模型以及基于小波分析（WA）的混合模型。此外，降噪和分解策略降低了序列的复杂性，降低了预测的难度。该模型具有有效的去噪和准确的分解能力，预测精度高，适用性广，在复杂时间序列预测中具有广阔的应用前景。这种新的预测模型是非线性预测模型的一种推广。
Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.
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