Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition

Wen chuan Wang, Kwok Wing Chau, Dong mei Xu, Xiao Yun Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

426 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)2655-2675
Number of pages21
JournalWater Resources Management
Issue number8
Publication statusPublished - 1 Jun 2015


  • Annual runoff forecasting
  • Auto-regressive integrated moving average (ARIMA)
  • Decomposition and ensemble
  • Ensemble empirical mode decomposition (EEMD)
  • Hydrologic time series

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Water Science and Technology


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