Multi-scale Foreign Exchange Rates Ensemble for Classification of Trends in Forex Market | Zendy

Hossein Talebi | Zendy; Winsor Hoang | Zendy; Marina L. Gavrilova | Zendy

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Multi-scale Foreign Exchange Rates Ensemble for Classification of Trends in Forex Market

Author(s) -

Hossein Talebi,

Winsor Hoang,

Marina L. Gavrilova

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.05.190

Subject(s) - foreign exchange market , computer science , artificial intelligence , foreign exchange , machine learning , naive bayes classifier , classifier (uml) , ensemble learning , bayesian probability , exchange rate , econometrics , support vector machine , economics , finance , monetary economics

Foreign exchange (Forex) market is the largest trading market in the world. Predicting the trend of the market and performing automated trading are important for investors. Recently, machine learning techniques have emerged as a powerful trend to predict foreign exchange (FX) rates. In this paper, we propose a new classification method for identifying up, down, and sideways trends in Forex market foreign exchange rates. A multi-scale feature extraction approached is used for training multiple classifiers for each trend. Bayesian voting is used to find the ensemble of classifiers for each trend. Performance of the system is validated using different metrics. The results show superiority of ensemble classifier over individual ones

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