Forecasting about EURJPY exchange rate using hidden Markova model and CART classification algorithm
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2015-02-22 https://doi.org/10.14419/jacst.v4i1.4194
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CART Classification Algorithm, EURJPY Exchange Rate, Forecasting, Foreign Exchange Market, Hidden Markov Model, Indicators, Neural Network. -
Abstract
The goal of this paper is forecasting direction (increase or decrease) of EURJPY exchange rate in a day. For this purpose five major indicators are used. The indicators are exponential moving average (EMA), stochastic oscillator (KD), moving average convergence divergence (MACD), relative strength index (RSI) and Williams %R (WMS %R). Then a hybrid approach using hidden Markov models and CART classification algorithms is developed. Proposed approach is used for forecasting direcation (increase or decrease) of Euro-Yen exchange rates in a day. Also the approach is used for forecasting differnece between intial and maximum exchange rates in a day. As well as it is used for forecasting differnece between intial and minimum exchange rates in a day. Reslut of proposed method is compared with CART and neural network. Comparison shows that the forecasting with proposed method has higher accuracy.
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How to Cite
Haeri, A., Hatefi, S. M., & Rezaie, K. (2015). Forecasting about EURJPY exchange rate using hidden Markova model and CART classification algorithm. Journal of Advanced Computer Science & Technology (JACST), 4(1), 84-89. https://doi.org/10.14419/jacst.v4i1.4194Received date: 2015-01-16
Accepted date: 2015-02-10
Published date: 2015-02-22