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.
Highlights
In international trading between two countries where one country has to make payments to another country in different currency, exchange rates are used
There are two major challenges associated with forecasting financial time series namely non-stationarity and the statistical property of the time series changing with time and non-linearity
The issue of non-linearity in forecasting financial time series can be addressed by using machine learning models Non-linear Autoregressive (NAR) and Support Vector Regression (SVR) to some extent
Summary
In international trading between two countries where one country has to make payments to another country in different currency, exchange rates are used. Exchange rates influence the trade balance, inflation, decisions of investors on foreign investments, worker remittances and the reserve position of a country. Knowing the future values of exchange rates, a financial time series is vital. Financial time series highly fluctuate and forecasting financial time series is a challenging task. There are two major challenges associated with forecasting financial time series namely non-stationarity and the statistical property of the time series changing with time and non-linearity. The issue of non-linearity in forecasting financial time series can be addressed by using machine learning models NAR and SVR to some extent. It was further attempted to improve the accuracy of forecasting the financial time series by introducing EMD (for eg. [14])
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