The Comparison between IABC with EGARCH in Foreign Exchange Rate Forecasting
Foreign exchange rate forecasting catches many researchers interests in recent years. Problems of the foreign exchange rate forecasting model selection and the improvement on forecasting accuracy are not easy to be solved. In this paper, the forecasting results obtained by conventional time-series models and by the Inter-active Artificial Bee Colony (IABC), which is a young artificial intelligent meth-od, are compared with each other with 4 years historical data. The sliding win-dow strategy is used in the experiment for both the training and the testing phases. In our experiments, we use continuous previous three days data as the training set, and use the training result to forecast the foreign exchange rate on the fourth day. In addition, 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 feeding macroeco-nomic factors to IABC as the input data is capable to produce higher accurate data in the foreign exchange rate than the conventional time-series models such as EGARCH.
KeywordsIABC Foreign Exchange Rate Forecasting Time-series EGARCH
Unable to display preview. Download preview PDF.
- 1.Abramson, D., Abela, J.: A parallel genetic algorithm for solving the school timetabling problem. In: Appeared in 15 Australian Computer Science Conference, Hobart, Australia, pp. 1–11 (1991)Google Scholar
- 2.Beni, G., Wang, J.: Swarm Intelligence in Cellular Robotic Systems. In: NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy (1989)Google Scholar
- 4.Branson, W.H.: Flow and stock equilibrium in a dynamic metzler model. Journal of Finance 31(5), 1323–1339 (1976)Google Scholar
- 5.Chang, J.-F., Chu, S.-C., Roddick, J.F., Pan, J.-S.: A parallel particle swarm optimization algorithm with communication strategies. Journal of Information Science and Engineering 21(4), 809–818 (2005)Google Scholar
- 11.Lin, K.-C., Chien, H.-Y.: CSO-based feature selection and parameter optimization for support vector machine. In: 2009 Joint Conferences on Pervasive Computing, Taipei, Taiwan, pp. 783–788 (2009)Google Scholar
- 18.Tsai, P.-W., Pan, J.-S., Liao, B.-Y.: Enhanced Artificial Bee Colony Optimization. International Journal of Innovative Computing, Information and Control ICIC International 5(12(B)), 5081–5092 (2009)Google Scholar
- 20.Whitley, D., Rana, S., Heckendorn, R.B.: The Island Model Genetic Algorithm: On Separability, Population Size and Convergence. Journal of Computing and Information Technology 1305/1997, 109–125 (1998)Google Scholar