Financial Time Series Forecasting Using Empirical Mode Decomposition and FNN: A Study on Selected Foreign Exchange Rates

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Abstract
The exchange rate, an economic indicator of the country is the relative price of one country’s currency in terms of another country’s currency. The stability of the exchange rate is important for a stable economic growth. Exchange rate series are non-linear and non-stationary. The fluctuations in the forecasting exchange rate are very important to the economy of the country. Researchers have proposed many hybrid machine learning models to get a more accurate forecast. This study proposes a hybrid forecasting model using Empirical Mode Decomposition (EMD) and Feedforward Neural Network (FNN) for foreign exchange rates forecasting and comparing its performance with widely used Non-linear Autoregressive (NAR) and Support Vector Regression (SVR) models. EMD is used to decompose the original non-linear and non-stationary series into several Intrinsic Mode Functions (IMFs) and one residual. The hybrid model is then used to forecast the exchange rate with IMFs and residual obtained as inputs. Empirical results obtained from forecasting daily exchange rates of Sri Lankan Rupees to Euro and Yen showed that the proposed EMD-FNN model outperforms NAR and SVR models without time series decomposition.