EXCHANGE RATE FORECASTING USING FUZZY TIME SERIES-MARKOV CHAIN
DOI:
https://doi.org/10.46754/umtjur.v3i3.230Keywords:
Exchange rate forecasting, fuzzy set theory, time series, fuzzy time series, markov chain, fuzzy time series Markov chain, MAPEAbstract
Exchange rate forecasting plays an important role in financial management. However, it is a complex process with high nonlinearity and data irregularity. Moreover, the forecasting of exchange rate is highly involved with imprecise and uncertain data. Analysis of forecasting models which corresponds to the exchange rate has always experienced fluctuations. Therefore, exchange rate forecasting becomes a challenging task in finance. Several studies have shown that stand-alone forecasting models such as time series, fuzzy time series, and Markov chain have their own drawbacks and are not successful enough in forecasting accurately. In this study, we propose a hybrid model of fuzzy time series-Markov chain to forecast the future exchange rate. Fuzzy time series-Markov chain is a combination of the classic fuzzy time series model with Markov chain model used to analyse a set of time series data. The main motivation for this study is to improve the accuracy in exchange rate forecasting. The selected currencies are Malaysian Ringgit (MYR) and Singapore Dollar (SGD). The proposed model was then evaluated by the Mean Absolute Percentage Error (MAPE) performance metric to test the robustness of the model. Lastly, a comparison between the proposed model and fuzzy time series model was conducted with respect to the MAPE. The results showed that the MAPE value for fuzzy time series-Markov chain was 0.9895% which fell under the criterion of highly accurate forecasting. Meanwhile, the MAPE value for fuzzy time series was 3.4306%. Thus, the forecasting performance of the proposed model was better than the fuzzy time series model. This study reveals the potential benefits of the proposed model as a highly accurate forecasting model.
References
Adesokan, I., Ngare, P., & Kilishi , A. (2017). Analyzing expected returns of a stock using the Markov chain model and the capital asset pricing model. Applied Mathematical Sciences, 11(56), 2777-2788. DOI: https://doi.org/10.12988/ams.2017.79287
Adhikari, R., & Agrawal, R. K. (2013). An introductory study on time series modeling and forecasting. LAP Lambert Academic Publishing, Germany.
Ali, O. A., Ali, A. Y., & Sumait, B. S. (2015). Comparison between the effects of different different types of membership functions on fuzzy logic controller performance. International Journal of Emerging Engineering Research and Technology, 3 (3), 76-83.
Avrachenkov, K. E., & Sanchez, E. (2002). Fuzzy Markov Chains and Decision-Making. Fuzzy Optimization and Decision Making, 1, 143-159. DOI: https://doi.org/10.1023/A:1015729400380
Boiroju, N. K., Rao, M. V., & Reddy, M. K. (2011). Forecasting Foreign Exchange Rates Using Fuzzy Time Series. International Journal of Statistics and Systems, 6(1), 153- 161.
Chang, S. S. L., & Zadeh, L. A. (1972). On fuzzy mapping and control. IEEE Transactions on Systems, Man, and Cybernetics, 2, 30–34. DOI: https://doi.org/10.1109/TSMC.1972.5408553
Chatfield, C. (2000). Time series forecasting. Chapman and Hall, London. DOI: https://doi.org/10.1201/9781420036206
Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81, 311-319. DOI: https://doi.org/10.1016/0165-0114(95)00220-0
Ching, W. K., & Ng, M. (2006). Markov chains: Models, algorithms and applications. Springer, New York.
Erdogan, O., & Goksu, A. (2014). Forecasting Euro and Turkish Lira exchange rates with Artificial Neural Networks (ANN). International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(4), 307–316. DOI: https://doi.org/10.6007/IJARAFMS/v4-i4/1361
He, Z. C., & Jiang , W. (2018). A new belief Markov chain model and its application in inventory prediction. International Journal of Production Research, 56(8), 2800-2817. DOI: https://doi.org/10.1080/00207543.2017.1405166
Islam, M. D., & Hossain, E. (2020). Foreign Exchange Currency Rate Prediction using a GRU-LSTM Hybrid Network. Soft Computing Letters, 1-20. DOI: https://doi.org/10.1016/j.socl.2020.100009
Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curvefittin. Butterworth Scientific, London.
Li, G., & Xiu, B. (2014). Fuzzy Markov chains based on the fuzzy transition probability. The 26th Chinese Control and Decision Conference, 4351-4356. DOI: https://doi.org/10.1109/CCDC.2014.6852945
Madi, E., Naim, S., Yaafar, A., Yaakob, A. M., & Yusoff, B. (2020). Agreement Matrix Based on Fuzzy DecisionMaking to Rank Ship Berthing Criteria. International Journal of Engineering Trends and Technology, 68(12), 31-36. DOI: https://doi.org/10.14445/22315381/IJETT-V68I12P206
Meyer, A., & Zimmermann, H. -J. (2011). Applications of Fuzzy Technology in Business Intelligence. International Journal of Computers, Communications & Control, 4(3), 428-441. DOI: https://doi.org/10.15837/ijccc.2011.3.2128
Reid, R. D., & Sanders, N. R. (2010). Operations management an integrated approach, 4th edition. Wiley, Hoboken, New Jersey.
Sahin, A., Kumbasar, T., Yesil, E., Dodurka, M. F., & Karasakal, O. (2014). An approach to represent time series forecasting via fuzzy numbers. 2nd International Conference on Artificial Intelligence, Modelling and Simulation, 51-56. DOI: https://doi.org/10.1109/AIMS.2014.36
Seneta, E. (2006). Markov and the the creation of Markov chains. MAM 2006: Markov Anniversary Meeting. Boson Books, Raleigh, North Carolina, 1-20.
Song, Q., & Chissom, B. S. (1993). Forecasting enrolments with fuzzy time series-Part I. Fuzzy Sets and Systems, 54(1), 1-9. DOI: https://doi.org/10.1016/0165-0114(93)90355-L
Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series-Part II. Fuzzy Sets and Systems, 62(1), 1-8. DOI: https://doi.org/10.1016/0165-0114(94)90067-1
Tayal, D., Sonawani, S., Ansari, G., & Gupta, C. (2011). Fuzzy time series forecasting of low dimensional numerical data. International Journal of Engineering Research and Applications, 2(1), 132-135.
Tsaur, R. C. (2012). A fuzzy time series-Markov chain model with an application to forecast the exchange rate between the Taiwan And US Dollar. International Journal of Innovative Computing, Information and Control, 8, 4931–4942.
Uzun, B., & Kıral, E. (2017). Application of Markov chains-fuzzy states to gold price. Procedia Computer Science, 120, 365-371 . DOI: https://doi.org/10.1016/j.procs.2017.11.251
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions On Systems, Man, and Cybernetics, Smc-3(1), 28-44. DOI: https://doi.org/10.1109/TSMC.1973.5408575
Zhang, D., & Zhang X. M. (2009). Study on forecasting the stock market trend based on stochastic analysis method. International Journal of Business and Management, 4(6). DOI: https://doi.org/10.5539/ijbm.v4n6p163
