@article{(Open Science Index):https://publications.waset.org/pdf/16439, title = {Designing Early Warning System: Prediction Accuracy of Currency Crisis by Using k-Nearest Neighbour Method}, author = {Nor Azuana Ramli and Mohd Tahir Ismail and Hooy Chee Wooi}, country = {}, institution = {}, abstract = {Developing a stable early warning system (EWS) model that is capable to give an accurate prediction is a challenging task. This paper introduces k-nearest neighbour (k-NN) method which never been applied in predicting currency crisis before with the aim of increasing the prediction accuracy. The proposed k-NN performance depends on the choice of a distance that is used where in our analysis; we take the Euclidean distance and the Manhattan as a consideration. For the comparison, we employ three other methods which are logistic regression analysis (logit), back-propagation neural network (NN) and sequential minimal optimization (SMO). The analysis using datasets from 8 countries and 13 macro-economic indicators for each country shows that the proposed k-NN method with k = 4 and Manhattan distance performs better than the other methods. }, journal = {International Journal of Mathematical and Computational Sciences}, volume = {7}, number = {7}, year = {2013}, pages = {1134 - 1139}, ee = {https://publications.waset.org/pdf/16439}, url = {https://publications.waset.org/vol/79}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 79, 2013}, }