Comparative Study: Common ANN and LS‐SVM Exchange Rate Performance Prediction. Issue 3 (1st May 2018)

• Record Type: Journal Article
• Authors: Sun, Ai; Zhao, Tianyi; Chen, Jungfang; Chang, Juifang
• Is Part Of: Chinese journal of electronics. Volume 27:Issue 3(2018)
• Page Start: 561
• Page End: 564
• Publication Date: 2018-05-01
• Subjects: Least squares‐Support vector machine (LS‐SVM) -- Neural network -- Structural risk function -- Characteristic space mapping -- Exchange rate prediction; exchange rates -- financial data processing -- least squares approximations -- neural nets -- support vector machines -- time series; exchange rate prediction performance -- parameter optimization method -- LS‐SVM prediction -- currency exchange rates -- ANN prediction results -- data‐driven neural network -- exchange rate time series data -- artificial neural network -- least squares‐support vector machine methods; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2018.01.003
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.