FORECASTING THE DEMAND OF BIRTH CONTROL PILLS USING ARIMA-GARCH

  • M Tio Putra Salis Faculty of Information Technology, Universitas Budi Luhur, Jakarta, Indonesia
  • Arif Bramantoro School of Computing and Informatics, Universiti Teknologi Brunei, Bandar Seri Begawan
Keywords: Forecasting, ARIMA-GARCH, birth control pill, supply, demand.

Abstract

The demand of birth control pill at National Population and Family Planning Agency in Ogan Komering Ilir district, South Sumatra, Indonesia is significantly high, and it varies from one sub-district to another. Consequently, supplying birth control pill becomes more complicated. To help this situation, an accurate forecasting on the demand of birth control pill is required. This paper proposes the combination of Autoregressive Integrated Moving Average (ARIMA) and Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) to obtain the best model for forecasting. The data set used is within the period of January 2017 and December 2019. Three experimental scenarios were proposed to achieve the best result: 1) 80% training data and 20% test data; 2) 75 training data and 25% test data; and 3) 66% training data and 34% test data. The best model obtained is characterized with ARIMA (1,1,2) and GARCH (2,1).

References

Indonesian Central Bureau of Statistics, 2020, ‘Population Census Results 2020’.
National Population and Family Planning Agency, 2021, ‘Vision and Mission’.
National Population and Family Planning Agency, 2013, ‘Monitoring of Couples of Childbearing Age Through Indonesia Mini Survey’, Jakarta.
Kawulur, L., Kundra,R., & Onibala, F., 2015, ‘Overview of the use of the pill kb in+ women aged fertile with hypertension in the region work Puskesmas Tanawangko subdistrict Tombariri’, Journal of keperawatan, 3(3), 1-5.
Montgomery, D. C., Jennings, C. L., & Kulahci, M., 2008, ‘Introduction to time series analysis and forecasting’, Hoboken, N.J: Wiley-Interscience.
Crawford, G. W., & Fratantoni, M. C., 2003, ‘Assessing the forecasting performance of regime‐switching, ARIMA and GARCH models of house prices’, Real Estate Economics, 31(2), 223-243.
Jaipuria, S., & Mahapatra, S.,2021, ‘A Hybrid Forecasting Technique to Deal with Heteroskedastic Demand in a Supply Chain’, Operations and Supply Chain Management: An International Journal, 14(2), 123-132.
Ghani, I. M., & Rahim, H. A., 2019, ‘Modeling and Forecasting of Volatility using ARMA-GARCH: Case Study on Malaysia Natural Rubber Prices’, In IOP Conference Series: Materials Science and Engineering, 548(1), p. 012023, IOP Publishing.
Ding, C., Duan, J., Zhang, Y., Wu, X., & Yu, G.,2017, ‘Using an ARIMA-GARCH modeling approach to improve subway short-term ridership forecasting accounting for dynamic volatility’, IEEE Transactions on Intelligent Transportation Systems, 19(4), 1054-1064.
Prabuwono, A. S., Usino, W., Yazdi, L., Basori, A. H., Bramantoro, A., Syamsuddin, I., ... & Allehaibi, K. H. S., ‘Automated visual inspection for bottle caps using fuzzy logic’, TEM Journal, 8(1), 107.
Hana, K. M., Al Faraby, S., & Bramantoro, A., 2020, ‘Multi-label classification of indonesian hate speech on twitter using support vector machines’, In 2020 International Conference on Data Science and Its Applications, 1-7, IEEE.
Alraouji, Y., & Bramantoro, A., 2014, ‘International call fraud detection systems and techniques’, In the 6th International Conference on Management of Emergent Digital EcoSystems, 159-166.
Adhikari, R., & Agrawal, R. K.,2013, ‘An introductory study on time series modeling and forecasting’, arXiv preprint, arXiv:1302.6613.
Agung, I. G. N.,2011,’Time series data analysis using Eviews’, John Wiley & Sons.
Published
2022-08-25
How to Cite
Putra Salis, M. T., & Bramantoro, A. (2022). FORECASTING THE DEMAND OF BIRTH CONTROL PILLS USING ARIMA-GARCH. Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2, 208-217. Retrieved from https://eprosiding.idbbali.ac.id/index.php/imade/article/view/728
Abstract dilihat 332 kali
FULL TEXT diunduh 257 kali