Foreign Exchange Rates Prediction Based on Comparative Models

Abstract

With the development of globalization, fluctuations in the foreign exchange market have the huge potential to influence global economy. Machine Learning technology shows great potential in the field of financial forecasting than traditional statistical methods in recent years. This research selected statistical model Autoregressive Integrated Moving Average (ARIMA) and machine learning models Linear Regression and Long- and Short-Term Memory (LSTM) to make training and testing of historical data on the forex market. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-Squared were chosen as evaluation indexes to evaluate the performance of each model and compare their performance in forex exchange rate forecasts. The results show that ARIMA and LSTM could make better predictions than Linear Regression, between which LSTM has higher degree of fitting data. This research provides a detailed analysis comparing the performance of different models in forex exchange rate forecasting whose results are beneficial to guide forex market forecast in practical application and provide reference for future research.