AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL
Outliers are observations that differ significantly from others that can affect the estimation results in the model and reduce the estimator's accuracy. To deal with outliers is to remove outliers from the data. However, sometimes important information is contained in the outlier, so eliminatin...
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Universitas Pattimura
2022-09-01
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6211 |
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| author | Nur'ainul Miftahul Huda Utriweni Mukhaiyar Nurfitri Imro'ah |
| author_facet | Nur'ainul Miftahul Huda Utriweni Mukhaiyar Nurfitri Imro'ah |
| author_sort | Nur'ainul Miftahul Huda |
| collection | DOAJ |
| description | Outliers are observations that differ significantly from others that can affect the estimation results in the model and reduce the estimator's accuracy. To deal with outliers is to remove outliers from the data. However, sometimes important information is contained in the outlier, so eliminating outliers is a misinterpretation. There are two types of outliers in the time series model, Innovative Outlier (IO) and Additive Outlier (AO). In the GSTAR model, outliers and spatial and time correlations can also be detected. We introduce an iterative procedure for detecting outliers in the GSTAR model. The first step is to form a GSTAR model without outlier factors. Furthermore, the detection of outliers from the model's residuals. If an outlier is detected, add an outlier factor into the initial model and estimate the parameters so that a new GSTAR model and residuals are obtained from the model. The process is repeated by detecting outliers and adding them to the model until a GSTAR model is obtained with no outliers detected. As a result, outliers are not removed or ignored but add an outlier factor to the GSTAR model. This paper presents case studies about Dengue Hemorrhagic Fever cases in five locations in West Kalimantan Province. These are the subject of the GSTAR model with adding outlier factors. The result of this paper is that using an iterative procedure to detect outliers based on the GSTAR residual model provides better accuracy than the regular GSTAR model (without adding outliers to the model). It can be solved without removing outliers from the data by adding outlier factors to the model. This way, the critical information in the outlier id is not lost, and an accurate ore model is obtained. |
| format | Article |
| id | doaj-art-5f50357ba1374629b0d49cfffc11f6f8 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-5f50357ba1374629b0d49cfffc11f6f82025-08-20T03:37:34ZengUniversitas PattimuraBarekeng1978-72272615-30172022-09-0116397598410.30598/barekengvol16iss3pp975-9846211AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODELNur'ainul Miftahul Huda0Utriweni Mukhaiyar1Nurfitri Imro'ah2Mathematics Department, Faculty of Mathematics and Natural Sciences, Universitas TanjungpuraStatistics Research Group, Faculty of Mathematics and Natural Sciences, Institut Teknologi BandungStatistics Department, Faculty of Mathematics and Natural Sciences, Universitas TanjungpuraOutliers are observations that differ significantly from others that can affect the estimation results in the model and reduce the estimator's accuracy. To deal with outliers is to remove outliers from the data. However, sometimes important information is contained in the outlier, so eliminating outliers is a misinterpretation. There are two types of outliers in the time series model, Innovative Outlier (IO) and Additive Outlier (AO). In the GSTAR model, outliers and spatial and time correlations can also be detected. We introduce an iterative procedure for detecting outliers in the GSTAR model. The first step is to form a GSTAR model without outlier factors. Furthermore, the detection of outliers from the model's residuals. If an outlier is detected, add an outlier factor into the initial model and estimate the parameters so that a new GSTAR model and residuals are obtained from the model. The process is repeated by detecting outliers and adding them to the model until a GSTAR model is obtained with no outliers detected. As a result, outliers are not removed or ignored but add an outlier factor to the GSTAR model. This paper presents case studies about Dengue Hemorrhagic Fever cases in five locations in West Kalimantan Province. These are the subject of the GSTAR model with adding outlier factors. The result of this paper is that using an iterative procedure to detect outliers based on the GSTAR residual model provides better accuracy than the regular GSTAR model (without adding outliers to the model). It can be solved without removing outliers from the data by adding outlier factors to the model. This way, the critical information in the outlier id is not lost, and an accurate ore model is obtained.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6211additivegstarinnovativeoutliers |
| spellingShingle | Nur'ainul Miftahul Huda Utriweni Mukhaiyar Nurfitri Imro'ah AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL Barekeng additive gstar innovative outliers |
| title | AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL |
| title_full | AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL |
| title_fullStr | AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL |
| title_full_unstemmed | AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL |
| title_short | AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL |
| title_sort | iterative procedure for outlier detection in gstar 1 1 model |
| topic | additive gstar innovative outliers |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6211 |
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