Open AccessForecasting the Exchange Rate for USD to RMB using RNN and SVM
Author(s) -
Ruofan Liao,
Petchaluck Boonyakunakorn,
Napat Harnpornchai,
Songsak Sriboonchitta
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1616/1/012050
Subject(s) - renminbi , support vector machine , autoregressive integrated moving average , exchange rate , depreciation (economics) , pound sterling , us dollar , currency , computer science , machine learning , benchmark (surveying) , artificial intelligence , time series , economics , finance , monetary economics , economic growth , capital formation , geodesy , financial capital , geography , human capital
One of the most important mechanisms supporting world trade is the exchange rate. Depreciation or appreciation of any currency, especially those main currencies such as US dollar, Pound sterling, Renminbi, could greatly affect international trade leading to greater impact to businesses and people’s wellbeing. Recently, researchers have been exploring the use of machine learning techniques to forecast time series data in the financial area. This paper will use machine learning techniques, namely Recurrent Neural Network (RNN), Support Vector Machine (SVM), and a traditional model, namely the ARIMA model which is selected as a benchmark. The result shows that RNN has the best performance compared with both SVM and ARIMA. This paper aims to forecast the exchange rate for USD to RMB, which could give the involved country, institution or people the foresight of the situation and prepare for risk.