Forecasting Dropout Trend at King’s College of The Philippines using ARIMA Modeling
Ruben M. Gambulao Jr
Abstract:
Student drop-outs continue to be one of the perineal problems of educational institutions. Accordingly, institution managers are trying to
find ways and means to curb impending issues on drop-outs to satisfy
quality education. In this paper, the researcher delved into the different
time series modeling methods in order to forecast the rate of college
dropouts at King’s College of the Philippines-Benguet. The method considered was the Autoregressive Integrated Moving Average (ARIMA)
model. The data used in this paper was the number of school dropouts
from 2005 to 2018 obtained from the school registrar which shows
more dropout during the first semester than the second semester. Initial result obtained from using ARIMA reveals that the best model used
is the model ARIMA which is the auto regression (AR 1), then the moving average (MA 1), with first differencing on the second semester.
References:
- Adedeji,A. A.,2016. “Trend Analysis of Students Dropout Rate and the Effects on the Social and Educational Systems in Nigeria.” Interna-tional Journal of Latest Research in Engineer-ing and Technology (IJLRET), ISSN: 24545031,Volume 2, Issue4.
- A.C. Orbeta, A.C. Jr.,2010. Global Study on Child Pov-erty and Disparities: Philippines”, NEDA, Ma-kati.
- Zhang,Y., 2009. “Cross-Validation for Selecting a Model Selection Procedure”, Lundquist Col-lege of Business University of Oregon.
- Pankratz,A., 1983 “Forecasting with Univariate Box-Jenkins Models,” John Wiley & Sons, Inc.
- Nau, R, 2014. “Introduction to ARIMA Models”, Fuqua School of Business Duke University.
- Ramos, P. et.al, 2016. “A Procedure for Identifica-tion of Appropriate State Space and ARIMA Models Based on Time-Series Cross-Validation”, Algorithms, Vol.9, Issue 4.
- Shao, J, 2013. “Linear Model Selection by Cross-Validation”, Journal of the American Statisti-cal Association, Vol. 88, No. 442.
- Box, G., et.al, 2008.Time Series Analysis, 4th ed.; Wiley: Hoboken, NJ, USA.
- Hyndman, R.J., 2006. Koehler, A.B. Another look at measures of forecast accuracy. Int. J. Fore-cast.
- Ugiliweneza,B.”Use of ARIMA Time Series and Re-gressors to Forecast the Sale of Electricity”, University of Louisville, Louisville KY. 11. R Core Team, 2017. R: A language and environ-ment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/
- Chen, J., 2019. What Does Autoregressive Integrat-ed Moving Average? Available at: https://Investopedia.com/
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