Using Interactive Artificial Bee Colony to Forecast Exchange Rate

Abstract

Exchange rate forecasting has become a popular research topic in recent years because the problems of the forecasting model selection and the improvement on forecasting accuracy are not easy to be solved. In this study, we employ a swarm intelligence method called Interactive Artificial Bee Colony (IABC) and use nine macroeconomic factors as the input for the exchange rate forecasting. The sliding window is used in the experiment for both the training and the testing. In our experiments, we use continuous previous three days data as the training set, and use the training result to forecast the fourth day's exchange rage. Moreover, we evaluate the forecasting accuracy with three criteria, namely, Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The experimental results indicate that using IABC with the macroeconomic factors is a positive and doable way for the exchange rate forecasting.