The exchange rate forecast is an important topic in international finance especially after the breakdown of the Bretton Woods system in 1973. Firms that involve in international business need to know the future exchange rate for various and accurate decision making in firms such as financing, investing and hedging. An accurate exchange rate forecast is not only important to firms involved in international business but also to households, governments and international organisations engage in international transaction [1]. Nonetheless, exchange rate forecast is not an easy task. An accurate forecast is unlikely to be obtained. There is no single forecasting method that is superior for obtaining accurate exchange rate all the time and for different exchange rate. Generally, exchange rate forecast methods can be classified according to technical forecasting, fundamental forecasting, marketbased forecasting, machine-learning based forecasting and mixed forecasting. Technical forecasting inspects the exchange rate history or studies the chart of exchange rate to find pattern that may recurrent in the future. Fundamental forecasting examines the relationship between exchange rate and other variable or investigates the intrinsic value of the exchange rate. Market-based forecasting explores the expectation of the market on the future exchange rate. Machine-learning based forecasting involves forecasting by using artificial neural network, which data are assumed to be non-linear [2]. Mixed forecasting is a composite of two or more methods. The same or different weight can be assigned to each method in mixed forecasting.

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