ABSTRACT
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 single HMM and HMM ensemble with neural network. HMM is trained by using forward-backward 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 March 2014. The preliminary results indicate that the new approach of HMM produce the lowest RMSE compared to the benchmark models. Further study is to adopt HMM-CBR in testing of GBP/USD, GBP/JPY, USD/JPY, and EUR/JPY exchange rate.
- Jyothi Badge, Forecasting of Indian Stock Market by Effective Macro-Economic Factors and Stochastic Model, Journal of Statistical and Econometric Methods, vol. 1, No. 2, 2012Google Scholar
- Magazine, 1986: p. 4--16. Rabiner, L. R, A tutorial on Hidden markov models and selected applications in speech recognition. IEEE, 1989. 77: p. 257--286.Google Scholar
- Yan Qi and Sherif Ishak, Application of Hidden Markov Models to Short-Term Speed Prediction during peak periods, Transportation Research Record Journal, 2010.Google Scholar
- Tarik Al-ani, Hidden Markov Models in Dynamic System Modelling and Diagnosis, INTECH, 2011Google Scholar
- Blaettler Florian et.al. Hidden Markov Models in Neurosciences, INTECH, 2011Google Scholar
- Md. Rafiul Hassan and Baikunth Nath, Stock Market Forecasting Using Hidden Markov Model: A New Approach, Proceedings of the 2005 5th International Conference on Intelligent Systems Design and Applications (ISDA'05) Google ScholarDigital Library
- Md. Rafiul Hassan, A combination of Hidden Markov Model and fuzzy model for stock market forecasting, Journal of Neurocomputing, pp. 3439--3446, 2009 Google ScholarDigital Library
- Jyothi Badge, Future State Prediction of Stock Market Using Hidden Markov Model Journal of Statistics and Systems, Volume 5, No. 1, 2010Google Scholar
- Ahani, E., and Abbas O, A Sequential Monte Carlo Approach for Online Stock Market Prediction Using Hidden Markov Model, 2010Google Scholar
- Aditya Gupta and Bhuwan Dhingra, Stock Market Prediction Using Hidden Markov Models, 2012Google Scholar
- Sang-Ho Park, Ju Hong Lee, and Hyo-Chan Lee, Trend forecasting of financial time series using PIPs detection and continuos HMM, Intelligent Data Analysis 15, 2011 Google ScholarDigital Library
- Ran El-Yaniv and Dmitry Pidan, Selective of Financial Trends with Hidden Markov Models, 2011Google Scholar
- Bushra Hossain, Mohiuddin, and Md. Fazle Rabbi, A novel approach for inflation analysis using Hidden Markov Model, IJCSI International Journal of Computer Science Issues, vol. 9, no. 2, 2012Google Scholar
- Imane Horiya Brahmi, Soufiene Djahel and Yacine Ghamri-Doudane, A Hidden Markov Model based Scheme for Efficient and Fast Dissemination of Safety Messages in VANETs, version 1, 2013Google Scholar
- P. Idval and C. Johnson, University Essay from Linkopings Universitet, Matematiska Institutionen, Linkopings Univesitet, 2008Google Scholar
- Adewole Adetunji Philip, Akinwale Adio Taofiki, and Akintomide Ayo Bidemi, Artificial Neural Network Model for forecasting foreign exchange rate, World of Computer Science and Information Technology Journal (WCSIT), vol. 1, no. 3, pp. 110--118, 2011.Google Scholar
- John Fallon, Making Profit in the Stock Market Using HMMs, 2012Google Scholar
- Diana Roman and Gautam Mitra, Hidden Markov Models for financial optimization problems, IMA Journal of Management Mathematics, vol. 21, issue 2, 2010Google ScholarCross Ref
- Tarik Al-ani, Hidden Markov Models in dynamic system modeling and diagnosis, In: Hidden Markov Models, Theory and Applications, Book edited by Dr. Przemyslaw Dymarski, pp. 25--66, April 2011Google Scholar
Index Terms
- A novel approach of hidden Markov model for time series forecasting
Recommendations
A combination of hidden Markov model and fuzzy model for stock market forecasting
This paper presents a novel combination of the hidden Markov model (HMM) and the fuzzy models for forecasting stock market data. In a previous study we used an HMM to identify similar data patterns from the historical data and then used a weighted ...
Hidden Markov model with missing emissions
AbstractIn a Hidden Markov model (HMM), from hidden states, the model generates emissions that are visible. Generally, the problems to be solved by such models, are based on such emissions that are considered as observed data. In this work, we propose to ...
Forecasting Change Directions for Financial Time Series Using Hidden Markov Model
RSKT '09: Proceedings of the 4th International Conference on Rough Sets and Knowledge TechnologyFinancial time series, i.e. stock prices, has the property of being noisy, volatile and non-stationary. It causes the uncertainty in the forecasting of the financial time series. To overcome this difficulty, we propose a new method that forecasts change ...
Comments