Forex prediction engine: framework, modelling techniques and implementations
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 r...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Inderscience Enterprises Ltd
2016
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| Subjects: | |
| Online Access: | http://eprints.sunway.edu.my/639/ http://eprints.sunway.edu.my/639/1/Tiong%2BNgo%2BLee%202016%20Forex%20prediction%20engine_%20deposited.pdf |
| _version_ | 1848801866646814720 |
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| author | Tiong, Leslie Ching Ow * Ngo, David Chek Ling * Lee, Yunli * |
| author_facet | Tiong, Leslie Ching Ow * Ngo, David Chek Ling * Lee, Yunli * |
| author_sort | Tiong, Leslie Ching Ow * |
| building | SU Institutional Repository |
| collection | Online Access |
| description | 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%. |
| first_indexed | 2025-11-14T21:14:16Z |
| format | Article |
| id | sunway-639 |
| institution | Sunway University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:14:16Z |
| publishDate | 2016 |
| publisher | Inderscience Enterprises Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | sunway-6392019-05-13T08:14:38Z http://eprints.sunway.edu.my/639/ Forex prediction engine: framework, modelling techniques and implementations Tiong, Leslie Ching Ow * Ngo, David Chek Ling * Lee, Yunli * HG Finance QA75 Electronic computers. Computer science QA76 Computer software 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%. Inderscience Enterprises Ltd 2016 Article NonPeerReviewed text en http://eprints.sunway.edu.my/639/1/Tiong%2BNgo%2BLee%202016%20Forex%20prediction%20engine_%20deposited.pdf 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 http://dx.doi.org/10.1504/IJCSE.2016.10001040 doi:10.1504/IJCSE.2016.10001040 |
| spellingShingle | HG Finance QA75 Electronic computers. Computer science QA76 Computer software Tiong, Leslie Ching Ow * Ngo, David Chek Ling * Lee, Yunli * Forex prediction engine: framework, modelling techniques and implementations |
| title | Forex prediction engine: framework, modelling techniques and implementations |
| title_full | Forex prediction engine: framework, modelling techniques and implementations |
| title_fullStr | Forex prediction engine: framework, modelling techniques and implementations |
| title_full_unstemmed | Forex prediction engine: framework, modelling techniques and implementations |
| title_short | Forex prediction engine: framework, modelling techniques and implementations |
| title_sort | forex prediction engine: framework, modelling techniques and implementations |
| topic | HG Finance QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.sunway.edu.my/639/ http://eprints.sunway.edu.my/639/ http://eprints.sunway.edu.my/639/ http://eprints.sunway.edu.my/639/1/Tiong%2BNgo%2BLee%202016%20Forex%20prediction%20engine_%20deposited.pdf |