Forecasting currency exchange rates using EMD-ARIMA Model

In today’s global economy, accuracy in forecasting currency exchange rates is of much importance to any future investment. Currency exchange rates portray non-linear and non-stationary characteristics hence to address these characteristics; this paper proposes a hybrid forecasting model using the Empirical Mode Decomposition (EMD) technique, and the ARIMA model. EMD is used to decompose the raw currency exchange rate data into several intrinsic mode functions and one residual. The process of extracting the IMFs from the data is called the sifting process. EMD was used to detect the moving trend of currency exchange rate data and improve the forecasting success of the ARIMA model. The data were obtained from the Central Bank of Kenya website between the periods January 2005 to May 2017. The best ARIMA model fitted to the raw data before decomposition based on information criterion statistics was found to be ARIMA (1,0,3) for the KShs/AE. Dirham, ARIMA (1,0,1) for KShs/Australian dollar and ARIMA (1,0,3) for KShs/Canadian dollar currency exchange rates. After forecasting, we then compared the forecasted values with the actual data to check the suitability of the ARIMA model. Further, EMD was applied to the exchange rate data and then fitted an ARIMA model to the IMFs. The best model was found to be ARIMA (1,0,1) for the KShs/AE. Dirham, ARIMA (0,0,1) for KShs/Australian dollar and ARIMA (1,0,1) for KShs/Canadian dollar currency exchange rates. The appropriateness of these models was tested using the Ljung-Box test. The forecasting performance of each model was evaluated using the RMSE.