Prediction of USD/JPY Exchange Rate Time Series Directional Status by KNN with Dynamic Time Warping AS Distance Function

Exchange rate prediction is a challenging topic in the recent decade. Various studies have been done to improve the prediction regarding the accuracy in terms of level error and directional status error. The aim of this paper is to introduce a methodology that uses KNN (K-nearest neighbors) and DTW (dynamic time warping) to improve the fluctuation prediction and to have better evaluation parameters in the literature of financial market forecasting, comparing to other researches. The study is done with USD/JPY(United States Dollar/Japanese Yen) exchange rate time series and the results show improvement of prediction regarding the direction of time series. USD/JPY exchange rates are gathered from 1971 to 2012 and are partitioned into 30 element segments regarding the monthly cyclic behavior of the time series. Then two different set of these 30 element segments are divided with 7:3 ratio and the KNN is used to find out the 3 nearest neighbors regarding the DTW as similarity function. By a chosen function introduced also in this research, the directional status of the last element is predicted and the prediction result is then compared with other results in the literature of exchange rate prediction.