A Study to Predict Eid Al-Fitr Day in Oman using Markov Chain Technique

Mohammed Razeeuddin *, B. V. Senthil Kumar**
*_**Section of Mathematics, Department of Information and Technology, Nizwa College of Technology (Nizwa), Sultanate of Oman.
Periodicity:January - March'2019
DOI : https://doi.org/10.26634/jmat.8.1.16233

Abstract

Markov chain is a powerful technique employed to forecast the variations in the sharemarket, customer behaviour, marketing, customers' brand loyalty, weather, game of golf, weather report, gold rate, conversion of currency rate, etc. This study focuses on research discrete-time Markov chain. Some significant properties of Markov chains are evoked and then the research and knowledge gained on Markov chains is applied to predict Eid al-Fitr day for the succeeding years and also in the long run. The Eid al-Fitr day for each year depends only on the previous year Eid al-Fitr day and not on its previous years' Eid al-Fitr days. The authors have also justified is study that the application of Markov chain is an appropriate process in predicting Eid al-Fitr day in Oman.

Keywords

Probability, Markov chain, Transition probability matrix

How to Cite this Article?

Razeeuddin, M., Kumar, B. V. S. (2019). A Study to Predict Eid al-Fitr Day in Oman using Markov Chain Technique. i-manager's Journal on Mathematics, 8(1), 8-15 https://doi.org/10.26634/jmat.8.1.16233

References

[1]. Bhusal, M. K. (2017). Application of Markov Chain Model in the Stock Market Trend Analysis of Nepal. International Journal of Scientific & Engineering Research, 8(10), 1733-1745.
[2]. Cao, L., & Tay, F. E. (2001). Financial forecasting using support vector machines. Neural Computing & Applications, 10(2), 184-192.
[3]. Choji, D. N., Eduno, S. N., & Kassem, G. T. (2013). Markov chain model application on share price movement in stock market. Computer Engineering and Intelligent Systems, 4(10), 84-95.
[4]. Drikpanchang. Retrieved from https://www.drikpanchang.com/calendars/indian/indiancalendar.html
[5]. Hao, F. (2006). The applications of Markov prediction method in stock market. Friends of Science, 6(1), 78-81.
[6]. Jasinthan, P., Laheetharan, A., & Satkunanathan, N. (2015). A Markov Chain Model for Vegetable Price Movement in Jaffna. Sri Lankan Journal of Applied Statistics, 16(2), 93-105.
[7]. Kakalejčík, L., Bucko, J., & Vejačka, M. (2019). Differences in buyer journey between high-and low-value customers of ecommerce business. Journal of Theoretical and Applied Electronic Commerce Research, 14(2), 47-58.
[8]. Kassa, A. M., Abrham, E., & Seid, T (2017). Application of Markov Chain Analysis Model for Predicting Monthly Market Share of Restaurants. International Journal of Recent Engineering Research and Development, 2(3), 48-55.
[9]. Otieno, S., Otumba, E. O., & Nyabwanga, R. N. (2015). Application of Markov chain to model and forecast stock market trend: A study of Safaricom shares in Nairobi Securities Exchange, Kenya. International Journal of Current Research, 7(4), 14712-14721.
[10]. Rajput, V., & Bobde, S. (2016). Stock market forecasting techniques: Literature survey. Int. J. Comput. Sci. Mob. Comput., 5(6), 500-506.
[11]. Sasikumar, R., & Abdullah, A. S. (2015). Applications of Various Stochastic Models In Financial Prediction. International Journal of Scientific and Innovative Mathematical Research, 3(3), 852-857.
[12]. Sharma, S. P., & Vishwakarma, Y. (2014). Application of Markov process in performance analysis of feeding system of sugar industry. Journal of Industrial Mathematics. Article ID 593176, 9 pages.
[13]. Wang, J. H., & Leu, J. Y. (1996, June). Stock market trend prediction using ARIMA-based neural networks. In Proceedings of International Conference on Neural Networks (ICNN'96) (Vol. 4, pp. 2160-2165). IEEE.
[14]. Wang, Z., Wang, T. K., & Yang, X. (1992). Birth and death processes and Markov chains. Springer (pp. 361).
[15]. Yan, Q., Qin, C., Nie, M., & Yang, L. (2018). Forecasting the electricity demand and market shares in retail electricity market based on system dynamics and Markov chain. Mathematical Problems in Engineering, Article ID 4671850, 11 pages.
[16]. Zhang, D., & Zhang, X. (2009). Study on forecasting the stock market trend based on stochastic analysis method. International Journal of Business and Management, 4(6), 163-170.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.