Outlier detection in time series model / Nurul Sima Mohamad Shariff ...[et al.]
Difficulty occurs in time series when the series are contaminated with outliers typically (i) Innovational Outlier (IO) and (ii) Additive Outlier (AO). As such, before estimating the parameters, one needs to overcome the effect of outliers. There are two approaches employed in this study to identify...
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| Format: | Conference or Workshop Item |
| Language: | en |
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2015
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| Online Access: | https://ir.uitm.edu.my/id/eprint/54079/1/54079.pdf https://ir.uitm.edu.my/id/eprint/54079/ |
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| _version_ | 1833070102548840448 |
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| author | Mohamad Shariff, Nurul Sima Hamzah, Nor Aishah Kamil, Karmila Hanim |
| author_facet | Mohamad Shariff, Nurul Sima Hamzah, Nor Aishah Kamil, Karmila Hanim |
| author_sort | Mohamad Shariff, Nurul Sima |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Difficulty occurs in time series when the series are contaminated with outliers typically (i) Innovational Outlier (IO) and (ii) Additive Outlier (AO). As such, before estimating the parameters, one needs to overcome the effect of outliers. There are two approaches employed in this study to identify outliers: (i) iterative outlier detection and joint parameter estimates proposed by Chen and Liu [2] and (ii) application of regression diagnostic tools. A simulation study is performed in an effort to assess the performance of both methods. The identification based on the regression diagnostic tools is seems superior compared to those proposed by Chen & Liu. The results also indicate that the proposed technique based on the regression diagnostic tools can be used to determine the outlier effects and the identification on the type of outlier. Moreover, it can also be applied to more complicated time series models that are widely use in practice particularly in the area of statistics research. |
| format | Conference or Workshop Item |
| id | my.uitm.ir-54079 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2015 |
| record_format | eprints |
| spelling | my.uitm.ir-540792023-02-22T07:27:57Z https://ir.uitm.edu.my/id/eprint/54079/ Outlier detection in time series model / Nurul Sima Mohamad Shariff ...[et al.] Mohamad Shariff, Nurul Sima Hamzah, Nor Aishah Kamil, Karmila Hanim T Technology (General) Technological change Difficulty occurs in time series when the series are contaminated with outliers typically (i) Innovational Outlier (IO) and (ii) Additive Outlier (AO). As such, before estimating the parameters, one needs to overcome the effect of outliers. There are two approaches employed in this study to identify outliers: (i) iterative outlier detection and joint parameter estimates proposed by Chen and Liu [2] and (ii) application of regression diagnostic tools. A simulation study is performed in an effort to assess the performance of both methods. The identification based on the regression diagnostic tools is seems superior compared to those proposed by Chen & Liu. The results also indicate that the proposed technique based on the regression diagnostic tools can be used to determine the outlier effects and the identification on the type of outlier. Moreover, it can also be applied to more complicated time series models that are widely use in practice particularly in the area of statistics research. 2015-11-04 Conference or Workshop Item PeerReviewed text en https://ir.uitm.edu.my/id/eprint/54079/1/54079.pdf Outlier detection in time series model / Nurul Sima Mohamad Shariff ...[et al.]. (2015) In: International Conference on Computing, Mathematics and Statistics (iCMS2015), 4-5 November 2015, Langkawi Lagoon Resort Langkawi Island, Kedah Malaysia. (Submitted) |
| spellingShingle | T Technology (General) Technological change Mohamad Shariff, Nurul Sima Hamzah, Nor Aishah Kamil, Karmila Hanim Outlier detection in time series model / Nurul Sima Mohamad Shariff ...[et al.] |
| title | Outlier detection in time series model / Nurul Sima Mohamad Shariff ...[et al.] |
| title_full | Outlier detection in time series model / Nurul Sima Mohamad Shariff ...[et al.] |
| title_fullStr | Outlier detection in time series model / Nurul Sima Mohamad Shariff ...[et al.] |
| title_full_unstemmed | Outlier detection in time series model / Nurul Sima Mohamad Shariff ...[et al.] |
| title_short | Outlier detection in time series model / Nurul Sima Mohamad Shariff ...[et al.] |
| title_sort | outlier detection in time series model / nurul sima mohamad shariff ...[et al.] |
| topic | T Technology (General) Technological change |
| url | https://ir.uitm.edu.my/id/eprint/54079/1/54079.pdf https://ir.uitm.edu.my/id/eprint/54079/ |
| url_provider | http://ir.uitm.edu.my/ |