Forecasting Foreign Exchange Rate with Machine Learning Techniques Chinese Yuan to US Dollar Using XGboost and LSTM Model

Abstract

Predicting exchange rates is important because it affects all major markets and plays a big part in the economy. The goal of this study is to forecast future values of the Chinese Yuan (CNY) to US dollar exchange rate. One of the most crucial aspects of the economy is the rate of exchange of currencies. The currency exchange rate is required in commercial terms, such as profit and investment evaluation. The purpose of CNY rate prediction is to determine the future value of the Chinese Yuan (CNY) relative to the US dollar, which can be taken into account while making decisions and lower the chance of losing money. As a result, we require a technique that can assist in accurately assisting in business selections regarding when to execute the appropriate deals. For this research study three-year dataset is used from 25 April 2020 to 26 May 2023. For this research we used two different machine learning models and comparison between the LSTM and Xgboost models first one is Long Short-Term Memory (LSTM) and second is Extreme Gradient Boosting (XGboost). The empirical results showed that the LSTM model provided better results than XGboost. Therefore, this study suggest that the LSTM model will helpful for the government monetary policymaker, economists and other stakeholders to identify and forecast the future trend of the exchange rate and make their policies accordingly.