Foreign exchange rates are among the most important economic indices in the international monetary markets. Applying forecasting models for forecasting in exchange rate markets and assisting investment decision making has become more indispensable in business practices than ever before. For large multinational firms, which conduct substantial currency transfers in the course of business, being able to accurately forecast movements of currency exchange rates can result in substantial improvement in the overall profitability of the firm. However, the literature shows that predicting the exchange rate movements are largely unforecastable due to their high volatility and noise and still are a problematic task. Many researches in time series forecasting have argued that predictive performance improves in combined models, especially when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most popular hybrid models categories, which have been shown to be successful for single models. However, they have yielded mixed results in some situations in comparison with components models used separately; and hence, it is not wise to apply them blindly to any type of data. In this paper, an enhanced version of hybrid neural based models is proposed, incorporating the autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs) for financial time series forecasting. In proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be guaranteed that the performance of the proposed model will not be worse than either of the components models used separately. In additional, empirical results in exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Therefore, it can be used as an appropriate alternative model for forecasting in exchange rate markets, especially when higher forecasting accuracy is needed.
Time series forecasting,Hybrid models; Artificial Neural Networks (ANNs),Auto-Regressive Integrated Moving Average (ARIMA),Exchange rate forecasting