Hidden Markov Models for Forex Trends Prediction

Author

Yunli Lee ; Ow, Leslie Tiong Ching ; Ling, David Ngo Chek

Author_Institution

Dept. of Comput. Sci. & Networked Syst., Sunway Univ., Petaling Jaya, Malaysia

fYear

2014

fDate

6-9 May 2014

Firstpage

1

Lastpage

4

Abstract

Foreign Exchange (Forex) market is a complex and challenging task for prediction due to uncertainty movement of exchange rate. However, these movements over timeframe also known as historical Forex data that offered a generic repeated trend patterns. This paper uses the features extracted from trend patterns to model and predict the next day trend. Hidden Markov Models (HMMs) is applied to learn the historical trend patterns, and use to predict the next day movement trends. We use the 2011 Forex historical data of Australian Dollar (AUS) and European Union Dollar (EUD) against the United State Dollar (USD) for modeling, and the 2012 and 2013 Forex historical data for validating the proposed model. The experimental results show outperforms prediction result for both years.

Keywords

economic forecasting; exchange rates; forecasting theory; hidden Markov models; Forex historical data; Forex trends prediction; HMMs; exchange rate; foreign exchange market; hidden Markov models; Artificial neural networks; Computational modeling; Feature extraction; Hidden Markov models; Market research; Predictive models; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Science and Applications (ICISA), 2014 International Conference on

Conference_Location

Seoul

Print_ISBN

978-1-4799-4443-9

Type

conf

DOI

10.1109/ICISA.2014.6847408

Filename

6847408