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The objective of this research was to investigate the most suitable forecasting model for three currency exchange rates (e.g. USD, SGD and JPY), which are time series from August 2012 to July 2017. The comparative study was used to find the most proper forecasting model from six forecasting models (e.g. simple exponential smoothing, Additive Holt-Winters, Multiplicative Holt-Winters, ARIMA, ARFIMA, and Feed-forward artificial neural network) based on five accuracy measures. The most suitable forecasting model plays a crucial role in international trade as well as effective import-export strategies of Thailand. The empirical results indicated that the feed–forward neural network outperforms all five statistical forecasting models, which revealed that the patterns of three currency exchange rates follow non-linear pattern rather than linear pattern. In addition, the developed model is formulated based on a few prior assumptions compared to statistical forecasting models. The model is able to construct complex predictive model to describe the currency exchange rates better than statistical forecasting models. Therefore, the proposed model can be a promising tool to predict the currency exchange rates, and to support decision making on effective import-export strategies of Thailand.
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