ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL
A range of spatio-temporal models has been used to model Covid-19 cases. However, there is only a small amount of literature on the analysis of estimating and forecasting Covid-19 cases using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model. This model is a develo...
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| Format: | Article |
| Language: | English |
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Universitas Diponegoro
2023-04-01
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| Series: | Media Statistika |
| Subjects: | |
| Online Access: | https://ejournal.undip.ac.id/index.php/media_statistika/article/view/45557 |
| _version_ | 1827587986822266880 |
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| author | Sukarna Sukarna Nurul Fadilah Syahrul Wahidah Sanusi Aswi Aswi Muhammad Abdy Irwan Irwan |
| author_facet | Sukarna Sukarna Nurul Fadilah Syahrul Wahidah Sanusi Aswi Aswi Muhammad Abdy Irwan Irwan |
| author_sort | Sukarna Sukarna |
| collection | DOAJ |
| description | A range of spatio-temporal models has been used to model Covid-19 cases. However, there is only a small amount of literature on the analysis of estimating and forecasting Covid-19 cases using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model. This model is a development of the GSTARMA model which has non-stationary data. This paper aims to estimate and forecast the daily number of Covid-19 cases in Sulawesi Island using GSTARIMA models. We compared two models namely GSTARI and GSTIMA considering the root mean square error (RMSE). Data on a daily number of Covid-19 cases (from April 10, 2020, to May 07, 2021) were used. The location weight used is the inverse distance weight based on the distance between airports in the capital cities of each province. The appropriate models obtained based on the data are the GSTARIMA (1;0;1;1) model and the GSTARIMA (1;1;1;0) model. The results showed that the forecast for the number of new Covid-19 cases is accurate and reliable only for the short term. |
| first_indexed | 2024-03-09T00:26:49Z |
| format | Article |
| id | doaj.art-ed4ab515b2a74f3d86b5d4f53c736222 |
| institution | Directory Open Access Journal |
| issn | 1979-3693 2477-0647 |
| language | English |
| last_indexed | 2024-03-09T00:26:49Z |
| publishDate | 2023-04-01 |
| publisher | Universitas Diponegoro |
| record_format | Article |
| series | Media Statistika |
| spelling | doaj.art-ed4ab515b2a74f3d86b5d4f53c7362222023-12-12T02:27:52ZengUniversitas DiponegoroMedia Statistika1979-36932477-06472023-04-0115218619710.14710/medstat.15.2.186-19721863ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELSukarna Sukarna0Nurul Fadilah Syahrul1Wahidah Sanusi2Aswi Aswi3Muhammad Abdy4Irwan Irwan5Mathematics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia, IndonesiaMathematics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia, IndonesiaMathematics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia, IndonesiaStatistics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia, IndonesiaMathematics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia, IndonesiaMathematics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia, IndonesiaA range of spatio-temporal models has been used to model Covid-19 cases. However, there is only a small amount of literature on the analysis of estimating and forecasting Covid-19 cases using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model. This model is a development of the GSTARMA model which has non-stationary data. This paper aims to estimate and forecast the daily number of Covid-19 cases in Sulawesi Island using GSTARIMA models. We compared two models namely GSTARI and GSTIMA considering the root mean square error (RMSE). Data on a daily number of Covid-19 cases (from April 10, 2020, to May 07, 2021) were used. The location weight used is the inverse distance weight based on the distance between airports in the capital cities of each province. The appropriate models obtained based on the data are the GSTARIMA (1;0;1;1) model and the GSTARIMA (1;1;1;0) model. The results showed that the forecast for the number of new Covid-19 cases is accurate and reliable only for the short term.https://ejournal.undip.ac.id/index.php/media_statistika/article/view/45557estimatingforecastinggstarimacovid-19 |
| spellingShingle | Sukarna Sukarna Nurul Fadilah Syahrul Wahidah Sanusi Aswi Aswi Muhammad Abdy Irwan Irwan ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL Media Statistika estimating forecasting gstarima covid-19 |
| title | ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL |
| title_full | ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL |
| title_fullStr | ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL |
| title_full_unstemmed | ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL |
| title_short | ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL |
| title_sort | estimating and forecasting covid 19 cases in sulawesi island using generalized space time autoregressive integrated moving average model |
| topic | estimating forecasting gstarima covid-19 |
| url | https://ejournal.undip.ac.id/index.php/media_statistika/article/view/45557 |
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