DOI: https://doi.org/10.14710/medstat.14.1.44-55

AUTOREGRESSIVE FRACTIONAL INTEGRATED MOVING AVERAGE (ARFIMA) MODEL TO PREDICT COVID-19 PANDEMIC CASES IN INDONESIA

*Puspita Kartikasari orcid scopus  -  Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Indonesia
Hasbi Yasin orcid scopus  -  Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Indonesia
Di Asih I Maruddani  -  Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Indonesia
Open Access Copyright (c) 2021 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Currently the emergence of the novel coronavirus (Sars-Cov-2), which causes the COVID-19 pandemic and has become a serious health problem because of the high risk causes of death. Therefore, fast and appropriate action is needed to reduce the spread of the COVID-19 pandemic. One of the way is to build a prediction model so that it can be a reference in taking steps to overcome them. Because of the nature of transmission of this disease which is so fast and massive cause extreme data fluctuations and between objects whose observational distances are far enough correlated with each other (long memory). The result of this determination is the best ARFIMA model obtained to predict additional of recovering cases of COVID-19 is (1,0,489.0) with an SMAPE value of 12,44%, while the case of death is (1.0.429.0) with SMAPE value of 13,52%. This shows that the ARFIMA model can accommodate well the long memory effect, resulting in a small bias. Also in estimating model parameters, it is also simpler. For cases of recovery and death, the number is increasing even though the case of death is still very high compared to cases of recovery.
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Keywords: ARFIMA; Prediction; COVID-19

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  1. Aye, G. C., Balcilar, M., Gupta, R., Kilimani, N., Nakumuryango, A., & Redford, S. (2014). Predicting BRICS stock returns using ARFIMA models. Applied Financial Economics, 24(17), 1159–1166. https://doi.org/10.1080/09603107.2014.924297
  2. Baillie, R. T., & Morana, C. (2012). Adaptive ARFIMA models with applications to inflation. Economic Modelling, 29(6), 2451–2459. https://doi.org/10.1016/j.econmod.2012.07.011
  3. Bhardwaj, G., & Swanson, N. R. (2006). An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series. Journal of Econometrics, 131(1–2), 539–578. https://doi.org/10.1016/j.jeconom.2005.01.016
  4. Chen, Q., Liang, M., Li, Y., Guo, J., Fei, D., Wang, L., He, L., Sheng, C., Cai, Y., Li, X., Wang, J., & Zhang, Z. (2020). Mental health care for medical staff in China during the COVID-19 outbreak. The Lancet Psychiatry, 7(4), e15–e16. https://doi.org/10.1016/S2215-0366(20)30078-X
  5. Chuang, A., & Wei, W. W. S. (1991). Time Series Analysis: Univariate and Multivariate Methods. In Technometrics (Vol. 33, Issue 1, p. 108). https://doi.org/10.2307/1269015
  6. Doornik, J. a, & Ooms, M. (1999). A Package for Estimating , Forecasting and Simulating Arfima Models : Arfima package 1 . 0 for Ox. Erasmus, 1–31
  7. Dueker, M., & Startz, R. (1998). Maximum-likelihood estimation of fractional cointegration with an application to U.S. and Canadian bond rates. Review of Economics and Statistics, 80(3), 420–426. https://doi.org/10.1162/003465398557654
  8. Fox, R., & Taqqu, M. S. (1991). Institute of Mathematical Statistics is collaborating with JSTOR to digitize, preserve, and extend access to The Annals of Statistics. ® www.jstor.org. Annals of Statistics, 19(3), 1403–1433
  9. Geweke, J., & Porter‐Hudak, S. (1983). the Estimation and Application of Long Memory Time Series Models. Journal of Time Series Analysis, 4(4), 221–238. https://doi.org/10.1111/j.1467-9892.1983.tb00371.x
  10. Hassler, U. (1993). Regression of Spectral Estimators With Fractionally Integrated Time Series. Journal of Time Series Analysis, 14(4), 369–380. https://doi.org/10.1111/j.1467-9892.1993.tb00151.x
  11. Hurvich, C. M., & Deo, R. S. (1999). Regression Estimates of the Memory Parameter of a Long-. Journal of Time Series Analysis, 1
  12. Kartikasari, P. (2020). Prediksi Harga Saham Pt . Bank Negara Indonesia Dengan Menggunakan Model Autoregressive Fractional Integrated Moving Average ( Arfima ). 8(1), 1–7
  13. Lobato, I., & Robinson, P. M. (1996). Averaged periodogram estimation of long memory. Journal of Econometrics, 73(1), 303–324. https://doi.org/10.1016/0304-4076(95)01742-9
  14. Mayoral, L. (2003). Centre de Referència en Economia Analítica Barcelona Economics Working Paper Series Working Paper no 55 Inequality. October 2001
  15. Reisen, V., & Abraham, B. (2001). Estimation of parameters in arfima processes: A simulation study. Communications in Statistics Part B: Simulation and Computation, 30(4), 787–803. https://doi.org/10.1081/SAC-100107781
  16. Robinson, P. M. (1995). Institute of Mathematical Statistics is collaborating with JSTOR to digitize, preserve, and extend access to The Annals of Statistics. ® www.jstor.org. Annals of Statistics, 19(3), 1403–1433
  17. S. Lopes., B. Olbermann., V. R. (2004). A Comparison of Estimation Methods in Non-Stationary ARFIMA Processes Lopes , S . R . C . 1–14
  18. Shi, Y., Wang, J., Yang, Y., Wang, Z., Wang, G., Hashimoto, K., Zhang, K., & Liu, H. (2020). Knowledge and Attitudes of Medical Staff in Chinese Psychiatric Hospitals Regarding COVID-19. Brain, Behavior, & Immunity - Health, 4(March), 100064. https://doi.org/10.1016/j.bbih.2020.100064
  19. Sifriyani, S., & Rosadi, D. (2020). Susceptible Infected Recovered (SIR) Model for Estimating Covid-19 Reproduction Number in East Kalimantan and Samarinda. Media Statistika, 13(2), 170–181. https://doi.org/10.14710/medstat.13.2.170-181
  20. Sowell, F. (1992). Maximum Likelihood Estimation of Stationary Univariate Fractionally Integrated Time Series Models. Journal of Econometrics, 53(1–3), 165–188. https://doi.org/10.1016/0304-4076(92)90084-5
  21. TAQQU, M. S., TEVEROVSKY, V., & WILLINGER, W. (1995). Estimators for Long-Range Dependence: an Empirical Study. Fractals, 03(04), 785–798. https://doi.org/10.1142/s0218348x95000692
  22. Torres-Reyna, O. (2013). PPT:Introduction to RStudio. Princeton University, August, 1–16
  23. Velasco, C. (1999). Gaussian Semiparametric Estimation of Non-Stationary Time Series. Journal of Time Series Analysis, 20(1), 87–127. https://doi.org/10.1111/1467-9892.00127
  24. Venables, W. N., Smith, D. M., & R Core Team. (2021). An Introduction to R. Quantitative Geography: The Basics, 0, 250–286. https://doi.org/10.4135/9781473920446.n12
  25. Who. (2020). A Coordinated Global Research Roadmap: 2019 Novel Coronavirus. In Who (Issue March). http://dx.doi.org/10.1038/s41591-020-0935-z
  26. Zheng, F., & Zhong, S. (2011). Time Series Forecasting Using A Hybrid RBF Neural Network and AR Model Based on Binomial Smoothing. World Academy of Science, Engineering and Technology, 51(3), 1464–1468. https://doi.org/10.5281/zenodo.1072611

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