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A four-stage hybrid model for hydrological time series forecasting.

Di C, Yang X, Wang X - PLoS ONE (2014)

Bottom Line: For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model.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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China.

ABSTRACT
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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Prediction results of the six series by using the proposed four-stage hybrid forecasting model and its three comparison methods.The proposed four-stage model has the form ‘denoising-decomposition-component prediction-ensemble’. This study utilizes EMD-based denoising method to denoise and decompose the denoised time series by EEMD, then predicts the IMFs by RBFNN and integrates the predicted results by LNN i.e. it has the form ‘EMD-EEMD-RBFNN-LNN’. As a comparison, the prediction results of its three comparison models (in different colors) are also given in this figure.
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pone-0104663-g008: Prediction results of the six series by using the proposed four-stage hybrid forecasting model and its three comparison methods.The proposed four-stage model has the form ‘denoising-decomposition-component prediction-ensemble’. This study utilizes EMD-based denoising method to denoise and decompose the denoised time series by EEMD, then predicts the IMFs by RBFNN and integrates the predicted results by LNN i.e. it has the form ‘EMD-EEMD-RBFNN-LNN’. As a comparison, the prediction results of its three comparison models (in different colors) are also given in this figure.

Mentions: The results of evaluating the forecasting of six cases are shown in Table 7, where the values in brackets are the prediction results of the ARIMA method as a comparison with the RBFNN model, the text in bold shows the results of the proposed EMD-EEMD-RBFNN-LNN model, and the other values are its comparison models. Fig. 8 shows the prediction results of the six cases by using the four-stage hybrid forecasting model, which contains the EMD-EEMD-RBFNN-LNN method and its three comparison models: EMD-EEMD-RBFNN-ADD, EMD-EEMD-ARIMA-LNN and EMD-WA-RBFNN-LNN.


A four-stage hybrid model for hydrological time series forecasting.

Di C, Yang X, Wang X - PLoS ONE (2014)

Prediction results of the six series by using the proposed four-stage hybrid forecasting model and its three comparison methods.The proposed four-stage model has the form ‘denoising-decomposition-component prediction-ensemble’. This study utilizes EMD-based denoising method to denoise and decompose the denoised time series by EEMD, then predicts the IMFs by RBFNN and integrates the predicted results by LNN i.e. it has the form ‘EMD-EEMD-RBFNN-LNN’. As a comparison, the prediction results of its three comparison models (in different colors) are also given in this figure.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4128719&req=5

pone-0104663-g008: Prediction results of the six series by using the proposed four-stage hybrid forecasting model and its three comparison methods.The proposed four-stage model has the form ‘denoising-decomposition-component prediction-ensemble’. This study utilizes EMD-based denoising method to denoise and decompose the denoised time series by EEMD, then predicts the IMFs by RBFNN and integrates the predicted results by LNN i.e. it has the form ‘EMD-EEMD-RBFNN-LNN’. As a comparison, the prediction results of its three comparison models (in different colors) are also given in this figure.
Mentions: The results of evaluating the forecasting of six cases are shown in Table 7, where the values in brackets are the prediction results of the ARIMA method as a comparison with the RBFNN model, the text in bold shows the results of the proposed EMD-EEMD-RBFNN-LNN model, and the other values are its comparison models. Fig. 8 shows the prediction results of the six cases by using the four-stage hybrid forecasting model, which contains the EMD-EEMD-RBFNN-LNN method and its three comparison models: EMD-EEMD-RBFNN-ADD, EMD-EEMD-ARIMA-LNN and EMD-WA-RBFNN-LNN.

Bottom Line: For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model.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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China.

ABSTRACT
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.

Show MeSH