Abstract
Hidden Markov Model is one of the most popular and broadly used for representation vastly structured series of data. This paper presents the application of the new approach of Hidden Markov Model and three ensemble nonlinear models to forecasting the foreign exchange rates. The proposed approach and other combination of computational intelligent techniques such as multi layer perceptron, support vector machine are compared with root mean squared error (RMSE) and Mean Absolute Error (MAE) as the performance measures. The results indicate that the new approach of Hidden Markov Model yield the best results consistently over all the currencies. and Case Based Reasoning based ensembles Based on the numerical experiments conducted, it is inferred that using the correct sophisticated ensemble methods in the computational intelligence paradigm can enhance the results obtained by the extent techniques to forecast foreign exchange rates. This suggests that the new approach of HMM is a powerful analytical instrument that is satisfactorily compared to using only the single model and other soft computing techniques for exchange rate predictions.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Badge, J.: Forecasting of indian stock market by effective macro- economic factors and stochastic model. J. Stat. Econom. Methods 1(2), 39–51 (2012)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989). Magazine, p. 4–16 (1986)
Qi, Y., Ishak, A.: Hidden Markov Model for short term prediction of traffic conditions on freeways. Transp. Res. Rec. J. 43(1), 95–111 (2014)
Al-ani, T.: Hidden Markov Models in dynamic system modelling and diagnosis. In: Dymarski, P. (ed.) Hidden Markov Models Theory and Applications, pp. 27–50. In Tech, Croatia (2011)
Florian, B. et al.: Hidden Markov Models in neurosciences. In: Dymarski, P. (ed.) Hidden Markov Models, Theory and Applications, p. 169. In Tech, Croatia (2011)
Hassan, M.R., Nath, B.: Stock market forecasting using hidden markov model: a new approach. In: Proceedings of the 2005 5th International Conference on Intelligent Systems Design and Applications (ISDA 2005)
Hassan, M.R.: A combination of hidden markov model and fuzzy model for stock market forecasting. J. Neurocomput. 72, 3439–3446 (2009)
Badge, J.: Future state prediction of stock market using hidden markov model. J. Stat. Syst. 5(1), 73–80 (2010)
Ahani, E., Abbas, O.: A sequential monte carlo approach for online stock market prediction using hidden markov model. J. Mod. Math. Stat. 4, 73–77 (2010)
Gupta, A., Dhingra, B.: Stock market prediction using Hidden Markov Models (2012)
Park, S.-H., Lee, J.H., Lee, H.-C.: Trend forecasting of financial time series using PIPs detection and continuos HMM. Intell. Data Anal. 15, 779–799 (2011)
El-Yaniv, R., Pidan, D.: Selective of financial trends with Hidden Markov Models (2011)
Hossain, B., Ahmed, M., Rabbi, MdF: A novel approach for inflation analysis using hidden markov model. IJCSI Int. J. Comput. Sci. Issues 9(2), 619 (2012)
Granger, C.W.J., Terasvirta, T.: Modelling Nonlinear Economic Relationships. Oxford University Press, Oxford (1993)
Aamodt, A., Plaza, E.: CBR: foundational issues, methodological variations and system approaches. AI Commun. 7(1), 39–59 (1994)
Hastie, T., Tibshirani, R., Fiedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2008)
Brahmi, I.H., Djahel, S., Ghamri-Doudane, Y.: A Hidden Markov Model based Scheme for Efficient and Fast Dissemination of Safety Messages in VANETs, version 1 (2013)
Yaser, S., Atiya, A.: Introduction to financial forecasting. Appl. Intell. 6, 205–213 (1996)
Bekaert, G., Wu, G.: Assymetry volatility and risk in equity market. Rev. Financ. Stud. 13(1), 1–42 (2000)
Hwang, H.B., Ang, H.T.: A simple neural network for ARMA (p,q) time series, OMEGA. Int J. Manag. Sci. 29, 319–333 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zahari, A., Jaafar, J. (2015). Combining Hidden Markov Model and Case Based Reasoning for Time Series Forecasting. In: Fujita, H., Selamat, A. (eds) Intelligent Software Methodologies, Tools and Techniques. SoMeT 2014. Communications in Computer and Information Science, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-17530-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-17530-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17529-4
Online ISBN: 978-3-319-17530-0
eBook Packages: Computer ScienceComputer Science (R0)