Study of Muslims in marital system using markov chain simple exponential smoothing (MCses) technique

Study of Muslims in marital system using markov chain simple exponential smoothing (MCses) technique

The Markov Chain (MC) model is a popular mathematical model used to observe the flow of data in a system. It can also be used to forecast future values for short-term period. However, most previous studies do not focus on the accuracy of the forecast values. Integration of the MC model and Simple Mo...

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Main Author: Fadhilah, Jamaluddin
Format: Thesis
Language:eng
eng
Published: 2016
Subjects:
QA273-280 Probabilities
Mathematical statistics
Online Access:https://etd.uum.edu.my/6988/1/s816704_01.pdf
https://etd.uum.edu.my/6988/2/s816704_02.pdf
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id my-uum-etd.6988
record_format uketd_dc
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Abdul Rahim, Rahela
topic QA273-280 Probabilities
Mathematical statistics
spellingShingle QA273-280 Probabilities
Mathematical statistics
Fadhilah, Jamaluddin
Study of Muslims in marital system using markov chain simple exponential smoothing (MCses) technique
description The Markov Chain (MC) model is a popular mathematical model used to observe the flow of data in a system. It can also be used to forecast future values for short-term period. However, most previous studies do not focus on the accuracy of the forecast values. Integration of the MC model and Simple Moving Average (SMA) technique is known to produce higher forecast accuracy than the classical MC model for the case of long-term projection with known previous data. However, Simple Exponential Smoothing (SES) technique is more flexible than SMA because it uses a smoothing constant. Therefore, this study develops modeling steps for MC model in the case of limited data and short-term projection by integrating MC model with SES (MCsEs). The MCsEs hybrid model is used to enhance the MC model and improve the accuracy of the forecast values. Four error measures used to determine the accuracy of this model are mean absolute deviation, mean absolute percentage deviation, mean absolute percentage error and mean square error. This study uses a sample of 6061 Muslim couples data in Pendang, Kedah who are in the marital system for the year 2013 and 2014. The number of Muslims in subsequence year according to gender and age categories is forecasted using proposed MCsEs hybrid model. Comparison with MC and MCsMA models indicates that the developed MCsEs hybrid model has better forecast accuracy. Therefore, the MCsEs hybrid model is the most appropriate model to forecast the number of Muslims in the marital system according to gender and age categories for the year 2014. This model can be used for short-term projection in cases with limited data and is applicable in various fields.
format Thesis
qualification_name masters
qualification_level Master's degree
author Fadhilah, Jamaluddin
author_facet Fadhilah, Jamaluddin
author_sort Fadhilah, Jamaluddin
title Study of Muslims in marital system using markov chain simple exponential smoothing (MCses) technique
title_short Study of Muslims in marital system using markov chain simple exponential smoothing (MCses) technique
title_full Study of Muslims in marital system using markov chain simple exponential smoothing (MCses) technique
title_fullStr Study of Muslims in marital system using markov chain simple exponential smoothing (MCses) technique
title_full_unstemmed Study of Muslims in marital system using markov chain simple exponential smoothing (MCses) technique
title_sort study of muslims in marital system using markov chain simple exponential smoothing (mcses) technique
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2016
url https://etd.uum.edu.my/6988/1/s816704_01.pdf
https://etd.uum.edu.my/6988/2/s816704_02.pdf
_version_ 1747828141197361152
spelling my-uum-etd.69882021-04-05T02:13:21Z Study of Muslims in marital system using markov chain simple exponential smoothing (MCses) technique 2016 Fadhilah, Jamaluddin Abdul Rahim, Rahela Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA273-280 Probabilities. Mathematical statistics The Markov Chain (MC) model is a popular mathematical model used to observe the flow of data in a system. It can also be used to forecast future values for short-term period. However, most previous studies do not focus on the accuracy of the forecast values. Integration of the MC model and Simple Moving Average (SMA) technique is known to produce higher forecast accuracy than the classical MC model for the case of long-term projection with known previous data. However, Simple Exponential Smoothing (SES) technique is more flexible than SMA because it uses a smoothing constant. Therefore, this study develops modeling steps for MC model in the case of limited data and short-term projection by integrating MC model with SES (MCsEs). The MCsEs hybrid model is used to enhance the MC model and improve the accuracy of the forecast values. Four error measures used to determine the accuracy of this model are mean absolute deviation, mean absolute percentage deviation, mean absolute percentage error and mean square error. This study uses a sample of 6061 Muslim couples data in Pendang, Kedah who are in the marital system for the year 2013 and 2014. The number of Muslims in subsequence year according to gender and age categories is forecasted using proposed MCsEs hybrid model. Comparison with MC and MCsMA models indicates that the developed MCsEs hybrid model has better forecast accuracy. Therefore, the MCsEs hybrid model is the most appropriate model to forecast the number of Muslims in the marital system according to gender and age categories for the year 2014. This model can be used for short-term projection in cases with limited data and is applicable in various fields. 2016 Thesis https://etd.uum.edu.my/6988/ https://etd.uum.edu.my/6988/1/s816704_01.pdf text eng public https://etd.uum.edu.my/6988/2/s816704_02.pdf text eng public masters masters Universiti Utara Malaysia Adeleke, R. A., Oguntuase, K. A., & Ogunsakin, R. E. (2014). Application of Markov Chain to the Assessment of Students' Admission and Academic Performance in Ekiti State University. International Journal of Scientific and Technology Research, 3(7), 349-3 57. Adnan, F. A. (2012). Projection Analysis of Postgraduate Students Flow in a University. (Unpublished master's thesis). Universiti Utara Malaysia, Kedah, Malaysia. Anusha, S. L., Alok, S., & Shaik, A. (2014). Demand Forecasting for the Indian Pharmaceutical Retail: A Case Study. Journal of Supply Chain Management Systems, 3(2), 1-8. Awadhi, S. A. Al, & Konsowa, M. (2007). An Application of Absorbing Markov Analysis to the Student Flow in an Academic Institution. Kuwait Journal of Science and Engineering, 34(2), 560-580. Borah, S., & Kakaty, S. C. (2012). Analysing Dynamics of Movement and Length-of-Stay of Patients Within a Hospital: A Markov Chain and a Semi-Markov Chain Approach. International Journal of Acadenzic Research, 4(2), 109-118. Bowerman, B. L., O'Connell, R. T., & Koehler, A. B. (2005). Forecasting, Time Series and Regression: An Applied Approach (4th ed.). Boston: Duxbury Press. Castaneda, L. B., Arunachalam, V., & Dharmaraja, D. (2012). Introduction to Probability and Stochastic Processes with Applications. New Jersey: John Wiley & Sons, Inc. Chat, C., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2001). A New Look at Models for Exponential Smoothing. Journal of the Roy Statistical Society, 50(2), 147-159. Chlen, S. C. H., & Mustaffa, M. S. (2008). Divorce in Malaysia. In Seminar Kaunseling Keluarga 2008 (pp. 23-28). Johor Choudhury, A,, & Jones, J. (2014). Crop Yield Prediction Using Time Series Models. Journal of Economic and Economic Education Research, 15(3), 53-68. Gaynor, P. E., & Kirkpatrick, R. C (1994). Introduction to Time-Series Modeling and Forecasting in Business and Economics. New York: McGraw-Hill, Inc. Giordano, F. R. (2003). A First Course in Mathematical Modelling. (M. Nance, Ed.) (3rd Edit.). USA: Pirtle, Bob. Hassan, M. (2010). A Discrete Time Markov Chain Model for Predicting the Duration of a Retail Mortgage in the Non-Default States. Academy of Banking Studies Journal, 9(1), 1-13. Horany, A., & Hassan, S. A. (2011). Marital Adjustment among Postgraduate Students at Universities in Malaysia. Elixir Psychology, 37, 3773-3776. Ibe, 0. C. (2009). Markov Processes for Stochastic Modeling. London: Elsevier Academic Press. Igboanugo, A. C., & Onifade, M. K. (2011). Markov Chain Analysis of Manpower Data of a Nigerian University. Journal of Innovative Research in Engineering and science, 2(2), 107-123. Incesu, G. (2012). Sales Forecasting System for Newspaper Distribution Companies in Turkey. Pakistan Journal of Statistics and Operation Research, 8(3), 685-699. Ismail, A,, & Hassan, N. I. (2013). Trend and Forecasting Rate of Cancer Deaths at a Public University Hospital Using Univariate Modeling. In International Conference on Mathematical Sciences and Statistics 201 3 (Vol. 1557, pp. 534-538). Selangor. doi: 10.1063/1.4823972 Lagarde, M., & Cairns, J. (2012). Modelling Human Resources Policies with Markov Models: An Illustration with the South African Nursing Labour Market. Health Care Management Science, 15(3), 270-82. doi: 10.1007/s10729-011-9184-5 Lazim, M. A. (2012). Introductory Business Forecasting: A Practical Approach (3rd ed.). Kuala Lumpur: UiTM Press. Liang, C., Cheng, G., Wixon, D. L., & Balser, T. C (2011). An Absorbing Markov Chain Approach to Understanding the Microbial Role in Soil Carbon Stabilization. Biogeochemistry, 106, 303-309. doi: 10.1007/s10533-010-9525-3 Maia, A. L. S., & Carvalho, F. de A. T. (2011). Holt's Exponential Smoothing and Neural Network Models for Forecasting Interval-Valued Time Series. International Journal of Forecasting, 27(3), 740-759. doi:l0.1016/j.ijforecast.2010.02.012 Manap, J., Kassim, A. C., Hoesni, S., Nen, S., Idris, F., & Ghazali, F. (2013). The Purpose of Marriage Among Single Malaysia Youth. Procedia - Social and Behavioral Sciences, 82, 112-116. doi: 10.1016/j.sbspro.2013.06.233 Nazim, A,, & Mhanorhan, A. (2014). A Comparison Between Single Exponential Smoothing (Ses), Double Exponential Smoothing (Des), Holt's (Brown) and Adaptive Response Rate Exponential Smothing (Arres) Techniques in Forecasting Malaysia Population. Global Journal of Mathematical Analysis, 2(4), 276-280. doi: 10.14419/gjma.v2i4.3253 Nicholls, M. G. (2009). The Use of Markov Models as an Aid to the Evaluation, Planning and Benchmarking of Doctoral Programs. Journal of the Operational Research Society, 60, 1183-1191. doi: 10.1057/palgrave.jors.2602639 Nor, A. M., Jaladin, R. A. M., Karim, D. N. F. M., & Ahmad, N. (2013). From Nasty to Healthy Divorce: A Marriage and Family Therapy Model for Working. Malaysian Online Journal of Counseling, 1(1), 1-23. Onder, E., & Kuzu, S. (2014). Forecasting Air T c Volumes Using Smoothing Techniques. Journal of Aeronautics and Space Technologies, 7(1), 65-85. Osarumwense, O. I. (2014). Time Series Forecasting Models : A Comparative Study of Some Models with Application to Inflation Data. Open Science Journal of Statistics and Application, 2(2), 24-29. Ostertagova, E., & Ostertag, O. (2011). The Simp Exponential Smoothing Model. In Modelling of Mechanical and Mechatronic Systems 2011 (pp. 380-384). Herlany: Technical University of Kosice. Ostertagova, E., & Ostertag, 0. (2012). Forecasting Using Simple Exponential Smoothing Method. Acta Electrotechnica et Informatica, 12(3), 62-66. doi: 10.2478/v10198-012-0034-2 Peng, Z., Yu, Z., Wang, H., & Yang, S. (2015). Research on Industrialization of Electric Vehicles with Its Demand Forecast Using Exponential Smoothing Method. Journal of Industrial Engineering and Management, 8(2), 365-382 doi:http://dx.doi.org/l0.3926/jiem.1287 Ravinder, H. V. (2013). Forecasting With Exponential Smoothing - What's the Right Smoothing Constant? Review of Business Information Systems - Third Quarter 2013, 17(3), 117-127. Saad, S. A. (2012). Academic Staff Flow Mode (Unpublished master's thesis). Universiti Utara Malaysia, Kedah, Malaysia. Saibeni, A. (20 10, April). Forecasting Accounts Receivable Collections With Markov Chains and Microsoft Excel. The Cpa Journal, 66-71. Retrieved from https://www.questia.com/magazine/1P3-2014009761/ forecasting-accounts-receivable-collections- with-markov Saidon, R., Adil, M. A. M., Sahri, M., Alias, B., d, N. M., & Murad, K. (2015). Developing a New Model of Underage Marriage Governance for Muslims in Malaysia. Middle-East Journal of Scientific Research, 23(4), 638-646. doi: 10.5829/idosi.mejsr.20l5.23.04.21762 Shah, C., & Burke, G. (1999). An Undergraduate Student Flow Model : Australian Higher Education. Higher Education, 37(4), 359-375. Skulj, D., Vehovar, V., & Stamfelj, D. (2008). The Modelling of Manpower by Markov Chains - A Case Study of the Slovenian Armed Forces. Informatica, 32, 289-291. Su, L. B. (2014). One Divorce in Malaysia Every 10 Minutes. Retrieved from http://www.themalaymailonline.com/malaysia/ article/one-divorce-in-malaysia-every-10- minutes. Taylor III, B. W. (2013). Introduction to Management Science. (S. Yagan, Ed.) (1lth ed.). New Jersey: Pearson Education, Inc. Taylor, H. M., & Karlin, S. (1998). An Introduction Stochastic Modeling (3rd ed.). London: Academic Press. Tersine, R. J., & Green, F. B. (1979). Exponential Smoothing: An Overview and Appraisal. lndustrial Management, 7-11. Zainab, I., Wan-Ibrahim, W. A,, & Asyraf, H. A. R. (2014). Marital Breakdown in Muslim Community in Malaysia. Middle-East Journal of Scientific Research, 20(1l), 1440-1444. doi: 10.5829/idosi.mejsr.2014.20.11.21072

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