Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis

In this paper, we propose a hybrid forecasting model that combines Empirical Mode Decomposition (EMD) with fast reduced kernel Extreme Learning Machine (KELM) for day ahead foreign currency exchange rate forecasting. EMD is an efficient method for nonlinear data decomposition in such a noisy environment and the purpose is to find important components in terms of Intrinsic Mode Functions (IMFs) by which the nonlinear time series is converted into stationary time series by making the data smoother and simpler for analysis. The average IMFs decomposed from EMD (AEMD) are hybridized with fast KELM named as AEMD-KELM for producing a more accurate forecast. The experimental results using AEMD-KELM method for seven currency exchange rates like CAD/HKD, CAD/CNY, CAD/USD, CAD/BRL, CAD/JPY, EUR/USD, and GBP/USD provide superior prediction and trend analysis in comparison with EMD based ELM (EMD-ELM) approaches. Further currency exchange rate movement trends are used for generating trading signals like buy, sell or hold.