Forex prediction engine: framework, modelling techniques and implementations

Tiong, Leslie Ching Ow * and Ngo, David Chek Ling * and Lee, Yunli * (2016) Forex prediction engine: framework, modelling techniques and implementations. International Journal of Computational Science and Engineering, 13 (4). pp. 364-377. ISSN 1742-7185

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Official URL: http://dx.doi.org/10.1504/IJCSE.2016.10001040

Abstract

Having accurate prediction in foreign exchange (Forex) market is useful because it provides intelligent information for investment strategy. This paper studies extracted repeating patterns of historical Forex time series, so to predict future trend direction by matching the forming trend with a repeating pattern. In the proposed Forex prediction engine, global pattern movements over a period of time are extracted using a linear regression line (LRL) enhanced technique, and then further segmented into what we called up and down curves. Subsequently, the artificial neural network (ANN) is applied to classify or group the uptrend and downtrend patterns. Finally, the dynamic time warping (DTW) is used through brute force to identify a trend pattern similar to the current trend at least for the beginning part. The remaining part of the matched pattern can provide predictive clues about next day trend movement. The experimental results generated on the dataset of AUD–USD and EUR–USD currencies between 2012 and 2013 demonstrate reliable accuracy performance of 72%.

Item Type: Article
Additional Information: First author is with KAIST; 2nd author is with the Vice-Chancellor Office, Sunway University; 3rd author is with Department of Computing and Information Systems, Faculty of Science and Technology, Sunway University,
Uncontrolled Keywords: Forex prediction engine; linear regression; artificial neural network; ANN; dynamic time warping; DTW.
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Others > Non Sunway Academics
Sunway University > School of Science and Technology > Dept. Computing and Information Systems
Sunway University > Vice-Chancellor Office
Depositing User: Ms. Molly Chuah
Related URLs:
Date Deposited: 30 Oct 2017 09:05
Last Modified: 13 May 2019 08:14
URI: http://eprints.sunway.edu.my/id/eprint/639

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