Treatment of outliers via interpolation method with neural network forecast performances
Outliers often lurk in many datasets, especially in real data. Such anomalous data can negatively affect statistical analyses, primarily normality, variance, and estimation aspects. Hence, handling the occurrences of outliers require special attention. Therefore, it is important to determine the sui...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2017
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| Online Access: | http://eprints.uthm.edu.my/5684/ http://eprints.uthm.edu.my/5684/1/AJ%202018%20%28305%29.pdf |
| _version_ | 1848888612486119424 |
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| author | Wahir, N. A. Nor, M. E. Rusiman, M. S. Gopal, K. |
| author_facet | Wahir, N. A. Nor, M. E. Rusiman, M. S. Gopal, K. |
| author_sort | Wahir, N. A. |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Outliers often lurk in many datasets, especially in real data. Such anomalous data can negatively affect statistical analyses, primarily normality, variance, and estimation aspects. Hence, handling the occurrences of outliers require special attention. Therefore, it is important to determine the suitable ways in treating outliers so as to ensure that the quality of the analyzed data is indeed high. As such, this paper discusses an alternative method to treat outliers via linear interpolation method. In fact, assuming outlier as a missing value in the dataset allows the application of the interpolation method to interpolate the outliers thus, enabling the comparison of data series using forecast accuracy before and after outlier treatment. With that, the monthly time series of Malaysian tourist arrivals from January 1998 until December 2015 had been used to interpolate the new series. The results indicated that the linear interpolation method, which was comprised of improved time series data, displayed better results, when compared to the original time series data in forecasting from both Box-Jenkins and neural network approaches. |
| first_indexed | 2025-11-15T20:13:03Z |
| format | Article |
| id | uthm-5684 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:13:03Z |
| publishDate | 2017 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-56842022-01-20T04:50:50Z http://eprints.uthm.edu.my/5684/ Treatment of outliers via interpolation method with neural network forecast performances Wahir, N. A. Nor, M. E. Rusiman, M. S. Gopal, K. QC994.95-999 Weather forecasting Outliers often lurk in many datasets, especially in real data. Such anomalous data can negatively affect statistical analyses, primarily normality, variance, and estimation aspects. Hence, handling the occurrences of outliers require special attention. Therefore, it is important to determine the suitable ways in treating outliers so as to ensure that the quality of the analyzed data is indeed high. As such, this paper discusses an alternative method to treat outliers via linear interpolation method. In fact, assuming outlier as a missing value in the dataset allows the application of the interpolation method to interpolate the outliers thus, enabling the comparison of data series using forecast accuracy before and after outlier treatment. With that, the monthly time series of Malaysian tourist arrivals from January 1998 until December 2015 had been used to interpolate the new series. The results indicated that the linear interpolation method, which was comprised of improved time series data, displayed better results, when compared to the original time series data in forecasting from both Box-Jenkins and neural network approaches. IOP Publishing 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/5684/1/AJ%202018%20%28305%29.pdf Wahir, N. A. and Nor, M. E. and Rusiman, M. S. and Gopal, K. (2017) Treatment of outliers via interpolation method with neural network forecast performances. Journal of Physics: Conference Series, 995. pp. 1-7. ISSN 1742-6588 |
| spellingShingle | QC994.95-999 Weather forecasting Wahir, N. A. Nor, M. E. Rusiman, M. S. Gopal, K. Treatment of outliers via interpolation method with neural network forecast performances |
| title | Treatment of outliers via interpolation method with neural network forecast performances |
| title_full | Treatment of outliers via interpolation method with neural network forecast performances |
| title_fullStr | Treatment of outliers via interpolation method with neural network forecast performances |
| title_full_unstemmed | Treatment of outliers via interpolation method with neural network forecast performances |
| title_short | Treatment of outliers via interpolation method with neural network forecast performances |
| title_sort | treatment of outliers via interpolation method with neural network forecast performances |
| topic | QC994.95-999 Weather forecasting |
| url | http://eprints.uthm.edu.my/5684/ http://eprints.uthm.edu.my/5684/1/AJ%202018%20%28305%29.pdf |