Elliott Wave Pattern Recognition for Forecasting GBP/USD Foreign Exchange Market

Abstract

This research presents an approach to the Elliott wave pattern implicates a forecast of future movements in foreign exchange (forex) rates of the previous movement inductive analysis. Elliott wave is defined that each individual wave has its own characteristic or pattern, which as expected reflects the psychology of the moment. The forex market is one of the utmost intricate markets through the characteristics of high volatility, nonlinearity and irregularity. Meantime, these characteristics also make it very difficult to forecast forex. The problem is contained pattern recognition, classification, and forecasting. The research objectives are to recognize the pattern using the Elliott wave pattern, to validate accuracy patterns classification using Linear Discriminant Analysis (LDA) and to forecast short-term forex market using Elliott wave method. LDA is employed to obtain in term of classification’s accuracy between 2 classes of selected data. The result shows the accuracy selected data is equal to 99.43%. Among of three levels of Fibonacci retracement which are 38.2%, 50.0%, and 61.8% results, the 38.2% shows the best forecasting for GBP/USD currency by using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (r) as the statistical measurements.