Hybridization of hidden markov model and case based reasoning for time series forecasting

Zahari, A.A. and Jaafar, J. (2014) Hybridization of hidden markov model and case based reasoning for time series forecasting. Frontiers in Artificial Intelligence and Applications, 265. pp. 63-74.

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In the past few years, tremendous studies have been made to examine the accuracy of time series forecasting that provide the foundation for decision models in foreign exchange data. This study proposes a novel approach of Hidden Markov Model and Case Based reasoning for time series forecasting. This paper compares the proposed method with the technical models; moving average convergence/divergence model (MACD), William's percent range, and naïve strategy for short-term trading decision. HMM is trained by using forwardbackward or Baum-Welch algorithm and the likelihood value is used to predict future exchange rate price. The forecasting accuracy has been measured according to Root Mean Square Error (RMSE). The statistical performance of all techniques is investigated in testing of EUR/USD exchange rate time series over the period of October 2010 to November 2013. The preliminary results indicate that the new approach of HMM produce the lowest RMSE compared to the benchmark models. Further study is to adopt Case Based reasoning to further improve the forecasting results. © 2014 The authors and IOS Press. All rights reserved.

Item Type: Article
Impact Factor: cited By 0
Uncontrolled Keywords: Case based reasoning; Finance; Forecasting; Markov processes; Mean square error; Time series, Baum-Welch algorithms; Convergence/divergence; Forecasting accuracy; Root mean square errors; Short-term trading; Statistical performance; Technical models; Time series forecasting, Hidden Markov models
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 29 Mar 2022 03:36
Last Modified: 29 Mar 2022 03:36
URI: http://scholars.utp.edu.my/id/eprint/31729

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