Forecasting about EURJPY exchange rate using hidden Markova model and CART classification algorithm

  • Authors

    • Abdorrahman Haeri School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
    • Seyed Morteza Hatefi Faculty of Engineering, Shahrekord University, Rahbar Boulevard, P.O. Box 115, Shahrekord, Iran
    • Kamran Rezaie School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
  • CART Classification Algorithm, EURJPY Exchange Rate, Forecasting, Foreign Exchange Market, Hidden Markov Model, Indicators, Neural Network.
  • 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, 4(1), 84-89.