Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31590
Title: Improving forecasting accuracy of annual runoff time series using arima based on eemd decomposition
Authors: Wang, WC
Chau, KW 
Xu, DM
Chen, XY
Keywords: Annual runoff forecasting
Auto-regressive integrated moving average (ARIMA)
Decomposition and ensemble
Ensemble empirical mode decomposition (EEMD)
Hydrologic time series
Issue Date: 2015
Publisher: Kluwer Academic Publishers
Source: Water resources management, 2015 How to cite?
Journal: Water Resources Management 
Abstract: Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.
URI: http://hdl.handle.net/10397/31590
ISSN: 0920-4741
DOI: 10.1007/s11269-015-0962-6
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