DOI: https://doi.org/10.14710/medstat.14.2.146-157

GSTARI-ARCH MODEL AND APPLICATION ON POSITIVE CONFIRMED DATA FOR COVID-19 IN WEST JAVA

Mutik Alawiyah  -  Master of Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia
Dianne Amor Kusuma  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia
*Budi Nurani Ruchjana orcid scopus publons  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia
Open Access Copyright (c) 2021 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Time series model that is commonly used is the Box-Jenkins based time series model. Time series data phenomena based on Box-Jenkins can be combined with spatial data, it is called the space time model One model based on Box-Jenkins model with heterogeneous location characteristics is the Generalized Space Time Autoregressive Integrated (GSTARI) model for a model that assumes data is not stationary or has a trend. This paper discusses the development of the GSTARI model with the assumption that the error variance is not constant which is applied to positive data confirmed by Covid-19 in West Java Province, especially in 4 regencies/cities that have cases in the high category from 6 March 2020 until 31 December 2020. Four regencies/cities are Depok City, Bekasi City, Bekasi Regency, and Karawang Regency. Parameter estimation method for the assumption of non-constant error variance can use Autoregressive Conditional Heteroscedasticity (ARCH) method. GSTARI-ARCH modeling procedure followed three Box-Jenkins stages, namely the identification process, parameter estimation and checking diagnostic. Application of the GSTARI-ARCH Model to Covid-19 positive confirmed data in 4 regencies/cities has a minimum value of RMSE in Bekasi City. The plot of forecast results for the four regencies/cities has a similar pattern to the actual data only applicable for a short time for 1-2 days.

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Keywords: ARCH; GSTARI; Positive Covid-19 Confirmed Cases; RMSE
Funding: Academic Leadership Grant Universitas Padjadjaran 2021 under contract 1959/UN6.3.1/PT.00/2021

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  1. Alawiyah, M., Kusuma, D. A. and Ruchjana, B. N. (2020) ‘Model Space-Time Autoregressive Integrated (Stari(2,1,1)) Untuk Data Debit Air Sungai Citarum Di Provinsi Jawa Barat’, BAREKENG: Jurnal Ilmu Matematika dan Terapan, 14(1), pp. 147–158. doi: 10.30598/barekengvol14iss1pp147-158
  2. Alawiyah, M., Kusuma, D. A. and Ruchjana, B. N. (2021) ‘Application of Generalized Space-Time Autoregressive Integrated (GSTARI) model in the phenomenon of covid-19’, Journal of Physics: Conference Series, 1722, p. 012035
  3. Apriliawan, D., Tarno and Yasin, H. (2013) ‘Pemodelan Laju Inflasi Di Provinsi Jawa Tengah Menggunakan Regresi Data Panel’, Jurnal Gaussian, 2(18), pp. 301–321
  4. Bonar, H., Nurani, B. and Darmawan, G. (2017) ‘Development of generalized Space-Time autoregressive integrated with ARCH error ( GSTARI – ARCH ) model based on consumer price index phenomenon at several cities in North Sumatera province’, 61(2012)
  5. Borovkova, S., Lopuhaä, H. P. and Ruchjana, B. N. (2008) ‘Consistency and asymptotic normality of least squares estimators in generalized STAR models’, Statistica Neerlandica, 62(4), pp. 482–508
  6. Box, G. E. P. and Jenkins, G. M. (1976) ‘Time Series Analysis: Forecasting and Control. 1st Edn’. Holden-Day, San Fransisco
  7. Data Terkonfirmasi Positif Covid-19 di Jawa Barat. 2020. Pikobar Jabar. URL: https://pikobar.jabarprov.go.id/data. (diakses tanggal 1 Januari 2021)
  8. Peta Jawa Barat. 2020. Wikipedia. URL: https://id.wikipedia.org/wiki/Daftar_kabupaten_dan_kota_di_Jawa_Barat. (diakses tanggal 1 Januari 2021)
  9. Fanelli, D. and Piazza, F. (2020) ‘Analysis and forecast of COVID-19 spreading in China, Italy and France’, Chaos, Solitons and Fractals, 134, p. 109761
  10. Habibie, G. and Perdana, H. (2020) ‘Penerapan Gstar-Sur Pada Jumlah Penumpang’, 09(2), pp. 275–284
  11. Iriany, A. et al. (2015) ‘GSTAR-SUR Model for Rainfall Forecasting in Tengger Region, East Java’, Int. Conf. Pure Appl. Res. Univ. Muhammadiyah Malang, (1), pp. 1–8
  12. Min, X., Hu, J. and Zhang, Z. (2010) ‘Urban traffic network modeling and short-term traffic flow forecasting based on GSTARIMA model’, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, pp. 1535–1540
  13. Nainggolan, N. et al. (2010) ‘Gstar Models With ARCH Errors and the Simulation’, (Icmns), pp. 1075–1084
  14. Pfeifer, P. E. and Deutsch, S. J. (1980) ‘A Three-Stage Iterative Procedure for Space-Time Modeling’, Technometrics, 22(1), pp. 35–47
  15. Roosa, K. et al. (2020) ‘Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020’, Infectious Disease Modelling, 5, pp. 256–263
  16. Ruchjana, B. (2002) A generalized space-time autoregressive model and its application to oil production
  17. Shereen, M. A. et al. (2020) ‘COVID-19 infection: origin, transmission, and characteristics of human coronaviruses’, Journal of Advanced Research, 24, pp. 91–98
  18. Suhartono et al. (2016) ‘GSTARX-GLS Model for Spatio-Temporal Data Forecasting’, Malaysian Journal of Mathematical Sciences, 10(January), pp. 91–103
  19. Wang, L.-S. et al. (2020) ‘A review of the 2019 Novel Coronavirus (COVID-19) based on current evidence.’, International journal of antimicrobial agents, p. 105948
  20. Wei, W. W. S. (2006) ‘Time Series Analysis: Univariate and Multivariate Methods (2nd Edition)’, in Pearson: Addison Wesley. second
  21. Wei, W. W. S. (2019) Multivariate Time Series Analysis and Applications. John Wiley & Son Ltd
  22. Zellner, A. (1962) ‘An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias’, Journal of the American Statistical Association, 57(298), pp. 348–368

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