EMD based hybrid models to forecast the KOSPI

코스피 예측을 위한 EMD를 이용한 혼합 모형

Kim, Hyowon;Seong, Byeongchan

  • Received : 2016.02.26
  • Accepted : 2016.02.29
  • Published : 2016.04.30


The paper considers a hybrid model to analyze and forecast time series data based on an empirical mode decomposition (EMD) that accommodates complex characteristics of time series such as nonstationarity and nonlinearity. We aggregate IMFs using the concept of cumulative energy to improve the interpretability of intrinsic mode functions (IMFs) from EMD. We forecast aggregated IMFs and residue with a hybrid model that combines the ARIMA model and an exponential smoothing method (ETS). The proposed method is applied to forecast KOSPI time series and is compared to traditional forecast models. Aggregated IMFs and residue provide a convenience to interpret the short, medium and long term dynamics of the KOSPI. It is also observed that the hybrid model with ARIMA and ETS is superior to traditional and other types of hybrid models.


intrinsic mode function;exponential smoothing method;ARIMA model;nonstationary model;nonlinear model


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Supported by : Chung-Ang University