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A Study on the Predictive Power Improvement of Time Series Model with Empirical Mode Decomposition Method
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 Title & Authors
A Study on the Predictive Power Improvement of Time Series Model with Empirical Mode Decomposition Method
Kim, Taereem; Shin, Hongjoon; Nam, Woosung; Heo, Jun-Haeng;
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The analysis of hydrologic time series data is crucial for the effective management of water resources. Therefore, it has been widely used for the long-term forecasting of hydrologic variables. In tradition, time series analysis has been used to predict a time series without considering exogenous variables. However, many studies using decomposition have been widely carried out with the assumption that one data series could be mixed with several frequent factors. In this study, the empirical mode decomposition method was performed for decomposing a hydrologic time series data into several components, and each component was applied to the time series models, autoregressive moving average (ARMA). After constructing the time series models, the forecasting values are added to compare the results with traditional time series model. Finally, the forecasted estimates from ARMA model with empirical mode decomposition method showed better performance than sole traditional ARMA model indicated from comparing the root mean square errors of the two methods.
empirical mode decomposition;intrinsic mode function;sifting algorithm;time series analysis;autoregressive moving average (ARMA) model;dam inflow forecasting;
 Cited by
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