Title: Forex prediction engine: framework, modelling techniques and implementations

Authors: Leslie C.O. Tiong; David C.L. Ngo; Yunli Lee

Addresses: KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea ' Office of Vice-Chancellor, Sunway University, No 5, Jalan Universiti, Bandar Sunway, 47500 Selangor, Malaysia ' Department of Computing and Information Systems, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor, Malaysia

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%.

Keywords: Forex prediction engine; linear regression; artificial neural networks; ANNs; dynamic time warping; DTW; modelling; foreign exchange markets; currencies; foreign exchange rates; intelligent information; investment strategy; exchange rate movements; exchange rate forecasting.

DOI: 10.1504/IJCSE.2016.080213

International Journal of Computational Science and Engineering, 2016 Vol.13 No.4, pp.364 - 377

Received: 29 Aug 2014
Accepted: 13 Dec 2014

Published online: 04 Nov 2016 *

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