Predicting the Japanese Yen to US Dollar Exchange Rate Based on Machine Learning Models

Time series data shows the dynamic changes of real-world data, such as temperature, rainfall, and Forex rate. Due to the complex changes in time series data, models differ from interpreting data best. For example, Auto-Regressive (AR) models are commonly used in stationary data. As for non-stationary, it performs worse and cannot predict the data well. This paper focuses on the changes in Foreign exchange data. Forex data is a non-stationary time series, making it difficult to model with stochastic process and stationary time series models because the number of features influencing the Forex rate is large. Such as the Gross domestic product (GDP), base interest rate, and the relation of the two countries. This research aims to classify the trend of future steps. Random Forest (RF) and Support Vector Machine (SVM) is used to compare the efficiency of modeling both multi-class and binary class classification when having a different number of input features. We also proposed a method to process the data to make the classification more accurate. The result shows that our method can increase performance efficiently.