DOI : https://doi.org/10.62527/ijasce.3.1.26

Forecasting Enrolment Data of Surigao del Sur State University, Philippines using Regression Analysis and Multiplicative Decomposition Model

Esmael V. Maliberan (1)
(1) CITE Department, Surigao del Sur State University, Tandag City, Surigao del Sur, 8300, Philippines
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How to cite (IJASEIT) :
Maliberan, E. V. (2021). Forecasting Enrolment Data of Surigao del Sur State University, Philippines using Regression Analysis and Multiplicative Decomposition Model. International Journal of Advanced Science Computing and Engineering, 3(1), 1–9. https://doi.org/10.62527/ijasce.3.1.26
Citation Format :
The study endeavored to predict the number of students who will enroll in Surigao del Sur State University using the historic data from 2010-2015. It was accomplished to utilize two existing methods to form its model in forecasting the enrolment data of the University. To project the number of students enrolled at the university, regression analysis, and a multiplicative decomposition model is used. The Mean Absolute Percentage Error (MAPE) of the forecasted enrolment data was 3.10 percent, according to the results. This only demonstrated that the estimated data were similar to the actual data, implying that it is accurate. The predicted enrolment data can be used to support decision-making and as an input to the University Development Plan.

SSG Abhishekn, and SR Singh, A score function-based method of forecasting using intuitionistic fuzzy time series, New Math. Nat. Computer 14 (1), 91-111, 2018.

Abhishekn, S. Gautam and SR. Singh, A refined method of forecasting based on high-order intuitionistic fuzzy time series data Progress in Artificial Intelligence vol.7, pp. 339-350, 2018.

SK. Bharati, Abhishekh, SR. Singh, A computational algorithm for the solution of fully fuzzy multi-objective linear programming problem, International Journal of Dynamics and Control vol. 6, no. 3, pp. 1384-1391, 2018.

S. Gautam and Abhishekn, A Novel Moving Average Forecasting Approach Using Fuzzy Time Series Data Set, Journal of Control, Automation and Electrical Systems, 2019.

R. Maliberan, Forecasting tourist arrival in the Province of Surigao del Sur, Philippines using Time Series Analysis, International Journal on Informatics Visualization, vol. 3, no. 3, 2019.

A. Talal, H. Ibraheem, and A. Salim, A, A Hybrid Algorithm to Forecast Enrolment Based on Genetic Algorithms and Fuzzy Time Series. IAJIT First Online Publication, 2013.

Y. Alyousifi, M. Othman, I. Faye, R. Sokkalingam and P. Silva, Markov Weighted Fuzzy Time-Series Model Based on an Optimum Partition Method for Forecasting Air Pollution, International Journal of Fuzzy Systems vol. 22, pp.1468–1486, 2020.

G, Dimaculangan, Using Regression Analysis to Forecast the Factors Affecting the Enrolment of a Tertiary School. Asia Pacific Journal of Multidisciplinary Research, vol. 2, no. 1, pp.121-130, 2014.

R. Lavilles, and M. Arcilla, Enrolment Forecasting for School Management System. IJMO, pp. 563-566, 2012.

S. Manganelli, A NEW THEORY OF FORECASTING. European Central Bank Working Paper Series, (584), 2006.

P. Brockwell, and R. Davis, Introduction to Time Series and Forecasting.

R. Hyndman, and G. Athanasopoulos, Forecasting Principles and Practice, 2018.

A. Ikuomola, and A. Adebiyi, A comparison of fuzzy time series and least-square method in forecasting students’ enrolment. Journal of Natural Sciences, Engineering And Technology, vol. 10, no. 1, pp. 19-30, 2011.

Z. Ismail, and R. Efendi, Enrollment Forecasting based on Modified Weight Fuzzy Time Series. Journal Of Artificial Intelligence, vol. 4, no. 1, pp. 110-118, 2011.

E. Bas, V. Uslu, U. Yolcu and E. Egrioglu, A Modified Genetic Algorithm for forecasting fuzzy time series, Applied Intelligence, vol. 41, no. 2, pp. 453-463, 2014.

H. Tang, and M. Yin, Forecasting performance of grey prediction for education expenditure and school enrollment. Economics of Education Review, vol. 31, no. 4, pp. 452-462, 2012.

N. Tinh, V. Vu and T. Linh, A New Method for Forecasting Enrolments Combining Time-Variant Fuzzy Logical Relationship Groups And K-Means Clustering. International Research Journal of Engineering and Technology (IRJET), 03(03), 2016.