Alternative method: outlier treatments with box-jenkins and neural network via interpolation method
Outliers represent the points that greatly diverge and act differently from the rest of the points. These kinds of phenomenon usually happen in the data especially in time series data. The presence of this outlier gave bad effect in all statistical method including forecasting if there are no action...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
UTHM Publisher
2018
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/5971/1/AJ%202017%20%28924%29.pdf http://eprints.uthm.edu.my/5971/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833417980193538048 |
|---|---|
| author | Wahir, Norsoraya Azurin Nor, Maria Elena Rusiman, Mohd Saifullah Pillay, Khuneswari Gopal |
| author_facet | Wahir, Norsoraya Azurin Nor, Maria Elena Rusiman, Mohd Saifullah Pillay, Khuneswari Gopal |
| author_sort | Wahir, Norsoraya Azurin |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Outliers represent the points that greatly diverge and act differently from the rest of the points. These kinds of phenomenon usually happen in the data especially in time series data. The presence of this outlier gave bad effect in all statistical method including forecasting if there are no actions on it. Thus, this paper discusses alternative methods which are linear interpolation and cubic spline interpolation to the time series data as outlier treatment. Assuming outlier as missing value in the data, the outlier were detected and the results were compared using forecast accuracies by two popular forecasting model, Box-Jenkins and neural network. The monthly time series data of Malaysia tourist arrival were used in this paper from 1998 until 2015. The result indicates that the improved time series data using the linear interpolation and cubic spline interpolation showed great performance in forecasting than the original data series. |
| format | Article |
| id | my.uthm.eprints-5971 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2018 |
| publisher | UTHM Publisher |
| record_format | eprints |
| spelling | my.uthm.eprints-59712022-01-25T02:30:20Z http://eprints.uthm.edu.my/5971/ Alternative method: outlier treatments with box-jenkins and neural network via interpolation method Wahir, Norsoraya Azurin Nor, Maria Elena Rusiman, Mohd Saifullah Pillay, Khuneswari Gopal TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks Outliers represent the points that greatly diverge and act differently from the rest of the points. These kinds of phenomenon usually happen in the data especially in time series data. The presence of this outlier gave bad effect in all statistical method including forecasting if there are no actions on it. Thus, this paper discusses alternative methods which are linear interpolation and cubic spline interpolation to the time series data as outlier treatment. Assuming outlier as missing value in the data, the outlier were detected and the results were compared using forecast accuracies by two popular forecasting model, Box-Jenkins and neural network. The monthly time series data of Malaysia tourist arrival were used in this paper from 1998 until 2015. The result indicates that the improved time series data using the linear interpolation and cubic spline interpolation showed great performance in forecasting than the original data series. UTHM Publisher 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5971/1/AJ%202017%20%28924%29.pdf Wahir, Norsoraya Azurin and Nor, Maria Elena and Rusiman, Mohd Saifullah and Pillay, Khuneswari Gopal (2018) Alternative method: outlier treatments with box-jenkins and neural network via interpolation method. Journal of Science and Technology, 10 (2). pp. 122-127. ISSN 2600-7924 |
| spellingShingle | TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks Wahir, Norsoraya Azurin Nor, Maria Elena Rusiman, Mohd Saifullah Pillay, Khuneswari Gopal Alternative method: outlier treatments with box-jenkins and neural network via interpolation method |
| title | Alternative method: outlier treatments with box-jenkins and neural network via interpolation method |
| title_full | Alternative method: outlier treatments with box-jenkins and neural network via interpolation method |
| title_fullStr | Alternative method: outlier treatments with box-jenkins and neural network via interpolation method |
| title_full_unstemmed | Alternative method: outlier treatments with box-jenkins and neural network via interpolation method |
| title_short | Alternative method: outlier treatments with box-jenkins and neural network via interpolation method |
| title_sort | alternative method: outlier treatments with box-jenkins and neural network via interpolation method |
| topic | TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks |
| url | http://eprints.uthm.edu.my/5971/1/AJ%202017%20%28924%29.pdf http://eprints.uthm.edu.my/5971/ |
| url_provider | http://eprints.uthm.edu.my/ |