Designing Early Warning System: Prediction Accuracy of Currency Crisis by Using k-Nearest Neighbour Method
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087229Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1928
 M. Gassner, B. Brabec, “Nearest neighbour models for local and regional avalanche forecasting,” Natural Hazards and Earth System Sciences, Vol. 2, pp. 247-253, 2002.
 Quansheng Kuang, Lei Zhao, “A practical GPU based kNN algorithm,” in Proc. 2nd International Computer Science and Computational Technology, China, 2009, pp. 151-155.
 Javier Arroyo, Carlos Maté, “Forecasting histogram time series with knearest neighbours methods,” International Journal of Forecasting, vol. 25, pp. 192-207, 2009.
 Madhavi Pradhan, “Design of Classifier for Detection of Diabetes using Neural Network and Fuzzy k-Nearest Neighbor Algorithm,” International Journal of Computational Engineering Research, vol. 2, no. 5, pp. 1384-1387, Sept. 2012.
 Jieh-Haur Chen, “KNN based knowledge-sharing model for severe change order disputes in construction,” Automation in Construction, vol. 17, pp. 773-779, 2008.
 Jae H. Min, Young-Chan Lee, “Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters,” Journal Expert Systems with Applications, vol.28, pp. 603-614, 2005.
 James C. Bezdek, Siew K. Chuah, “Generalized k-nearest neighbour rules,” Fuzzy Sets and Systems, vol.18, no.3, pp. 237-256, 1986.
 Shixin Yu et al., “Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery,” Pattern Recognition Letters, vol. 23, pp. 183-190, 2002.
 Lamartine Almeida Teixeira, Adriano Lorena, “A method for automatic stock trading combining technical analysis and nearest neighbor classification,” Expert Systems with Applications, vol. 37, pp. 6885- 6890, 2010.
 S. Arya et al., “An optimal algorithm for approximate nearest neighbor searching fixed dimensions,” Journal of the ACM, vol. 45, no. 6, pp. 891-923, 1998.
 K. Q.. Weinberger, L.K. Saul, “Distance metric learning for large margin nearest neighbor classification,” Journal of Machine Learning Research, vol.10, pp. 207-244, 2009.
 J. Goldberger et al., “Neighbourhood components analysis,” in Proceedings of the Conference on Neural Information Processing Systems, 2004.
 Lutz Hamel, Knowledge Discovery with Support Vector Machines. New Jersey: A John-Wiley & Sons, 2009.
 Ethem Alpaydin, Introduction to Machine Learning, 2nd ed. Massachusetts: The MIT Press, 2010.
 Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York: Springer-Verlag, 2008.
 C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer-Verlag, 2007.
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