%0 Journal Article %A Nor Azuana Ramli and Mohd Tahir Ismail and Hooy Chee Wooi %D 2013 %J International Journal of Mathematical and Computational Sciences %B World Academy of Science, Engineering and Technology %I Open Science Index 79, 2013 %T Designing Early Warning System: Prediction Accuracy of Currency Crisis by Using k-Nearest Neighbour Method %U https://publications.waset.org/pdf/16439 %V 79 %X 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. %P 1134 - 1139