Open AccessComparative Study: Common ANN and LS‐SVM Exchange Rate Performance Prediction
Author(s) -
Sun Ai,
Zhao Tianyi,
Chen Jungfang,
Chang Juifang
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.01.003
Subject(s) - support vector machine , artificial neural network , computer science , exchange rate , least squares support vector machine , machine learning , artificial intelligence , data mining , term (time) , physics , quantum mechanics , economics , macroeconomics
Due to the mathematical modeling principle deficiency, the data‐driven neural network and support vector machine methods have become the powerful basic methods for the exchange rate prediction. Based on the analysis of the characteristics of exchange rate time series data, the exchange rate prediction performance of Artificial neural network (ANN) and Least squares‐Support vector machine (LS‐SVM) is explored. The parameter optimization method of the two‐times training is proposed. The fundamental principle of LS‐SVM prediction is analysed in detail. By virtue of daily, monthly and quarterly data of three currency exchange rates, the prediction performance of LS‐SVM is examined. The comparison is made with ANN prediction results based on the same data in relevant literature review. According to the experimental result, LS‐SVM has better short‐term prediction performance, and it is superior to ANN in most cases in terms of prediction precision.