Improving prediction of exchange rates using Differential EMD

• Published in: Expert systems with applications 2013-01, Vol.40 (1), p.377-384
• Main Authors: Premanode, Bhusana, Toumazou, Chris
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer simulation; Empirical Mode Decomposition; Exact sciences and technology; Exchange rates; Inference from stochastic processes; time series analysis; Linear inference, regression; Markov processes; Markov switching GARCH; Markov switching regression; Mathematical analysis; Mathematical models; Mathematics; Operational research and scientific management; Operational research. Management science; Portfolio theory; Prediction; Probability and statistics; Regression; Sciences and techniques of general use; Statistics; Support vector regression; Time series; Vectors (mathematics)
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.07.048

► The algorithm of Differential EMD has the capability of smoothing and reducing the noise. ► The SVR model can predict exchange rates. ► In prediction, the Differential EMD and SVR model outperforms the MS-GARCH model. Volatility is a key parameter when measuring the size of errors made in modelling returns and other financial variables such as exchanged rates. The autoregressive moving-average (ARMA) model is a linear process in time series; whilst in the nonlinear system, the generalised autoregressive conditional heteroskedasticity (GARCH) and Markov switching GARCH (MS-GARCH) have been widely applied. In statistical learning theory, support vector regression (SVR) plays an important role in predicting nonlinear and nonstationary time series variables. In this paper, we propose a new algorithm, differential Empirical Mode Decomposition (EMD) for improving prediction of exchange rates under support vector regression (SVR). The new algorithm of Differential EMD has the capability of smoothing and reducing the noise, whereas the SVR model with the filtered dataset improves predicting the exchange rates. Simulations results consisting of the Differential EMD and SVR model show that our model outperforms simulations by a state-of-the-art MS-GARCH and Markov switching regression (MSR) models.